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Journal articles on the topic 'Adversarial Channels'

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Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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1

Suresh, Vinayak, Eric Ruzomberka, Chih-Chun Wang, and David J. Love. "Causal Adversarial Channels With Feedback Snooping." IEEE Journal on Selected Areas in Information Theory 3, no. 1 (March 2022): 69–84. http://dx.doi.org/10.1109/jsait.2022.3158230.

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2

Wang, Pengwei, and Reihaneh Safavi-Naini. "A Model for Adversarial Wiretap Channels." IEEE Transactions on Information Theory 62, no. 2 (February 2016): 970–83. http://dx.doi.org/10.1109/tit.2015.2503766.

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3

Simon, Paul, Scott Graham, Christopher Talbot, and Micah Hayden. "Model for Quantifying the Quality of Secure Service." Journal of Cybersecurity and Privacy 1, no. 2 (May 7, 2021): 289–301. http://dx.doi.org/10.3390/jcp1020016.

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Although not common today, communications networks could adjust security postures based on changing mission security requirements, environmental conditions, or adversarial capability, through the coordinated use of multiple channels. This will require the ability to measure the security of communications networks in a meaningful way. To address this need, in this paper, we introduce the Quality of Secure Service (QoSS) model, a methodology to evaluate how well a system meets its security requirements. This construct enables a repeatable and quantifiable measure of security in a single- or multi-channel network under static configurations. In this approach, the quantification of security is based upon the probabilities that adversarial listeners and disruptors may gain access to or manipulate transmitted data. The initial model development, albeit a snap-shot of the network security, provides insights into what may affect end-to-end security and to what degree. The model was compared against the performance and expected security of several point-to-point networks, and three simplified architectures are presented as examples. Message fragmentation and duplication across the available channels provides a security performance trade-space, with an accompanying comprehensive measurement of the QoSS. The results indicate that security may be improved with message fragmentation across multiple channels when compared to the number of adversarial listeners or disruptors. This, in turn, points to the need, in future work, to build a full simulation environment with specific protocols and networks to validate the initial modeled results.
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4

Anantharamu, Lakshmi, Bogdan S. Chlebus, and Mariusz A. Rokicki. "Adversarial Multiple Access Channels with Individual Injection Rates." Theory of Computing Systems 61, no. 3 (November 28, 2016): 820–50. http://dx.doi.org/10.1007/s00224-016-9725-x.

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5

Pan, Xuran, Fan Yang, Lianru Gao, Zhengchao Chen, Bing Zhang, Hairui Fan, and Jinchang Ren. "Building Extraction from High-Resolution Aerial Imagery Using a Generative Adversarial Network with Spatial and Channel Attention Mechanisms." Remote Sensing 11, no. 8 (April 15, 2019): 917. http://dx.doi.org/10.3390/rs11080917.

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Segmentation of high-resolution remote sensing images is an important challenge with wide practical applications. The increasing spatial resolution provides fine details for image segmentation but also incurs segmentation ambiguities. In this paper, we propose a generative adversarial network with spatial and channel attention mechanisms (GAN-SCA) for the robust segmentation of buildings in remote sensing images. The segmentation network (generator) of the proposed framework is composed of the well-known semantic segmentation architecture (U-Net) and the spatial and channel attention mechanisms (SCA). The adoption of SCA enables the segmentation network to selectively enhance more useful features in specific positions and channels and enables improved results closer to the ground truth. The discriminator is an adversarial network with channel attention mechanisms that can properly discriminate the outputs of the generator and the ground truth maps. The segmentation network and adversarial network are trained in an alternating fashion on the Inria aerial image labeling dataset and Massachusetts buildings dataset. Experimental results show that the proposed GAN-SCA achieves a higher score (the overall accuracy and intersection over the union of Inria aerial image labeling dataset are 96.61% and 77.75%, respectively, and the F1-measure of the Massachusetts buildings dataset is 96.36%) and outperforms several state-of-the-art approaches.
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6

Safavi-Naini, Reihaneh, and Pengwei Wang. "A Model for Adversarial Wiretap Channels and its Applications." Journal of Information Processing 23, no. 5 (2015): 554–61. http://dx.doi.org/10.2197/ipsjjip.23.554.

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7

Leung, Debbie, and Graeme Smith. "Communicating Over Adversarial Quantum Channels Using Quantum List Codes." IEEE Transactions on Information Theory 54, no. 2 (February 2008): 883–87. http://dx.doi.org/10.1109/tit.2007.913433.

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8

Chlebus, Bogdan S., Dariusz R. Kowalski, and Mariusz A. Rokicki. "Maximum throughput of multiple access channels in adversarial environments." Distributed Computing 22, no. 2 (August 27, 2009): 93–116. http://dx.doi.org/10.1007/s00446-009-0086-4.

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9

Sutanto, Richard Evan, and Sukho Lee. "Real-Time Adversarial Attack Detection with Deep Image Prior Initialized as a High-Level Representation Based Blurring Network." Electronics 10, no. 1 (December 30, 2020): 52. http://dx.doi.org/10.3390/electronics10010052.

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Several recent studies have shown that artificial intelligence (AI) systems can malfunction due to intentionally manipulated data coming through normal channels. Such kinds of manipulated data are called adversarial examples. Adversarial examples can pose a major threat to an AI-led society when an attacker uses them as means to attack an AI system, which is called an adversarial attack. Therefore, major IT companies such as Google are now studying ways to build AI systems which are robust against adversarial attacks by developing effective defense methods. However, one of the reasons why it is difficult to establish an effective defense system is due to the fact that it is difficult to know in advance what kind of adversarial attack method the opponent is using. Therefore, in this paper, we propose a method to detect the adversarial noise without knowledge of the kind of adversarial noise used by the attacker. For this end, we propose a blurring network that is trained only with normal images and also use it as an initial condition of the Deep Image Prior (DIP) network. This is in contrast to other neural network based detection methods, which require the use of many adversarial noisy images for the training of the neural network. Experimental results indicate the validity of the proposed method.
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10

Ahlswede, Rudolf, Igor Bjelaković, Holger Boche, and Janis Nötzel. "Quantum Capacity under Adversarial Quantum Noise: Arbitrarily Varying Quantum Channels." Communications in Mathematical Physics 317, no. 1 (November 20, 2012): 103–56. http://dx.doi.org/10.1007/s00220-012-1613-x.

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11

Anantharamu, Lakshmi, Bogdan S. Chlebus, Dariusz R. Kowalski, and Mariusz A. Rokicki. "Packet latency of deterministic broadcasting in adversarial multiple access channels." Journal of Computer and System Sciences 99 (February 2019): 27–52. http://dx.doi.org/10.1016/j.jcss.2018.07.001.

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12

Dapogny, Arnaud, Matthieu Cord, and Patrick Perez. "The Missing Data Encoder: Cross-Channel Image Completion with Hide-and-Seek Adversarial Network." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 10688–95. http://dx.doi.org/10.1609/aaai.v34i07.6696.

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Image completion is the problem of generating whole images from fragments only. It encompasses inpainting (generating a patch given its surrounding), reverse inpainting/extrapolation (generating the periphery given the central patch) as well as colorization (generating one or several channels given other ones). In this paper, we employ a deep network to perform image completion, with adversarial training as well as perceptual and completion losses, and call it the “missing data encoder” (MDE). We consider several configurations based on how the seed fragments are chosen. We show that training MDE for “random extrapolation and colorization” (MDE-REC), i.e. using random channel-independent fragments, allows a better capture of the image semantics and geometry. MDE training makes use of a novel “hide-and-seek” adversarial loss, where the discriminator seeks the original non-masked regions, while the generator tries to hide them. We validate our models qualitatively and quantitatively on several datasets, showing their interest for image completion, representation learning as well as face occlusion handling.
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13

Sun, Chonggao, Yonghong Chen, Hui Tian, and Shuhong Wu. "Covert Timing Channels Detection Based on Auxiliary Classifier Generative Adversarial Network." IEEE Open Journal of the Computer Society 2 (2021): 407–18. http://dx.doi.org/10.1109/ojcs.2021.3131598.

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14

Sarkar, Rupak, and Ashiqur R. KhudaBukhsh. "Are Chess Discussions Racist? An Adversarial Hate Speech Data Set (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 15881–82. http://dx.doi.org/10.1609/aaai.v35i18.17937.

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On June 28, 2020, while presenting a chess podcast on Grandmaster Hikaru Nakamura, Antonio Radic's YouTube handle got blocked because it contained ``harmful and dangerous'' content. YouTube did not give further specific reason, and the channel got reinstated within 24 hours. However, Radic speculated that given the current political situation, a referral to ``black against white'', albeit in the context of chess, earned him this temporary ban. In this paper, via a substantial corpus of 681,995 comments, on 8,818 YouTube videos hosted by five highly popular chess-focused YouTube channels, we ask the following research question: \emph{how robust are off-the-shelf hate-speech classifiers to out-of-domain adversarial examples?} We release a data set of 1,000 annotated comments where existing hate speech classifiers misclassified benign chess discussions as hate speech. We conclude with an intriguing analogy result on racial bias with our findings pointing out to the broader challenge of color polysemy.
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15

Sucholutsky, Ilia, and Matthias Schonlau. "SecDD: Efficient and Secure Method for Remotely Training Neural Networks (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 15897–98. http://dx.doi.org/10.1609/aaai.v35i18.17945.

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We leverage what are typically considered the worst qualities of deep learning algorithms - high computational cost, requirement for large data, no explainability, high dependence on hyper-parameter choice, overfitting, and vulnerability to adversarial perturbations - in order to create a method for the secure and efficient training of remotely deployed neural networks over unsecure channels.
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16

Sengupta, S., S. Anand, K. Hong, and R. Chandramouli. "On Adversarial Games in Dynamic Spectrum Access Networking based Covert Timing Channels?" ACM SIGMOBILE Mobile Computing and Communications Review 13, no. 2 (September 25, 2009): 96–107. http://dx.doi.org/10.1145/1621076.1621086.

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17

Yuan, X., J. Tian, and P. Reinartz. "GENERATING ARTIFICIAL NEAR INFRARED SPECTRAL BAND FROM RGB IMAGE USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORK." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 279–85. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-279-2020.

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Abstract. Near infrared bands (NIR) provide rich information for many remote sensing applications. In addition to deriving useful indices to delineate water and vegetation, near infrared channels could also be used to facilitate image pre-processing. However, synthesizing bands from RGB spectrum is not an easy task. The inter-correlations between bands are not clearly identified in physical models. Generative adversarial networks (GAN) have been used in many tasks such as generating photorealistic images, monocular depth estimation and Digital Surface Model (DSM) refinement etc. Conditional GAN is different in that it observes some data as a condition. In this paper, we explore a cGAN network structure to generate a NIR spectral band that is conditioned on the input RGB image. We test different discriminators and loss functions, and evaluate results using various metrics. The best simulated NIR channel has a mean absolute error of around 5 percent in Sentinel-2 dataset. In addition, the simulated NIR image can correctly distinguish between various classes of landcover.
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18

Li, Guanyue, Qianfen Jiao, Sheng Qian, Si Wu, and Hau-San Wong. "High Fidelity GAN Inversion via Prior Multi-Subspace Feature Composition." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 8366–74. http://dx.doi.org/10.1609/aaai.v35i9.17017.

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Generative Adversarial Networks (GANs) have shown impressive gains in image synthesis. GAN inversion was recently studied to understand and utilize the knowledge it learns, where a real image is inverted back to a latent code and can thus be reconstructed by the generator. Although increasing the number of latent codes can improve inversion quality to a certain extent, we find that important details may still be neglected when performing feature composition over all the intermediate feature channels. To address this issue, we propose a Prior multi-Subspace Feature Composition (PmSFC) approach for high-fidelity inversion. Considering that the intermediate features are highly correlated with each other, we incorporate a self-expressive layer in the generator to discover meaningful subspaces. In this case, the features at a channel can be expressed as a linear combination of those at other channels in the same subspace. We perform feature composition separately in the subspaces. The semantic differences between them benefit the inversion quality, since the inversion process is regularized based on different aspects of semantics. In the experiments, the superior performance of PmSFC demonstrates the effectiveness of prior subspaces in facilitating GAN inversion together with extended applications in visual manipulation.
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19

Chen, Jiahao, Chong Wu, Hu Chen, and Peng Cheng. "Unsupervised Dark-Channel Attention-Guided CycleGAN for Single-Image Dehazing." Sensors 20, no. 21 (October 23, 2020): 6000. http://dx.doi.org/10.3390/s20216000.

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In this paper, we propose a new unsupervised attention-based cycle generative adversarial network to solve the problem of single-image dehazing. The proposed method adds an attention mechanism that can dehaze different areas on the basis of the previous generative adversarial network (GAN) dehazing method. This mechanism not only avoids the need to change the haze-free area due to the overall style migration of traditional GANs, but also pays attention to the different degrees of haze concentrations that need to be changed, while retaining the details of the original image. To more accurately and quickly label the concentrations and areas of haze, we innovatively use training-enhanced dark channels as attention maps, combining the advantages of prior algorithms and deep learning. The proposed method does not require paired datasets, and it can adequately generate high-resolution images. Experiments demonstrate that our algorithm is superior to previous algorithms in various scenarios. The proposed algorithm can effectively process very hazy images, misty images, and haze-free images, which is of great significance for dehazing in complex scenes.
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20

Yadav, Jyoti Deshwal, Vivek K. Dwivedi, and Saurabh Chaturvedi. "ResNet-Enabled cGAN Model for Channel Estimation in Massive MIMO System." Wireless Communications and Mobile Computing 2022 (August 29, 2022): 1–9. http://dx.doi.org/10.1155/2022/2697932.

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Massive multiple-input multiple-output (MIMO), or large-scale MIMO, is one of the key technologies for future wireless networks to exhibit a large accessible spectrum and throughput. The performance of a massive MIMO system is strongly reliant on the nature of various channels and interference during multipath transmission. Therefore, it is important to compute accurate channel estimation. This paper considers a massive MIMO system with one-bit analog-to-digital converters (ADCs) on each receiver antenna of the base station. Deep learning (DL)-based channel estimation framework has been developed to reduce signal processing complexity. This DL framework uses conditional generative adversarial networks (cGANs) and various convolutional neural networks, namely reverse residual network (reverse ResNet), squeeze-and-excitation ResNet (SE ResNet), ResUNet++, and reverse SE ResNet, as the generator model of cGAN for extracting the features from the quantized received signals. The simulation results of this paper show that the trained residual block-based generator model of cGAN has better channel generation performance than the standard generator model in terms of mean square error.
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21

Bache, Florian, Christina Plump, Jonas Wloka, Tim Güneysu, and Rolf Drechsler. "Evaluation of (power) side-channels in cryptographic implementations." it - Information Technology 61, no. 1 (February 25, 2019): 15–28. http://dx.doi.org/10.1515/itit-2018-0028.

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Abstract Side-channel attacks enable powerful adversarial strategies against cryptographic devices and encounter an ever-growing attack surface in today’s world of digitalization and the internet of things. While the employment of provably secure side-channel countermeasures like masking have become increasingly popular in recent years, great care must be taken when implementing these in actual devices. The reasons for this are two-fold: The models on which these countermeasures rely do not fully capture the physical reality and compliance with the requirements of the countermeasures is non-trivial in complex implementations. Therefore, it is imperative to validate the SCA-security of concrete instantiations of cryptographic devices using measurements on the actual device. In this article we propose a side-channel evaluation framework that combines an efficient data acquisition process with state-of-the-art confidence interval based leakage assessment. Our approach allows a sound assessment of the potential susceptibility of cryptographic implementations to side-channel attacks and is robust against noise in the evaluation system. We illustrate the steps in the evaluation process by applying them to a protected implementation of AES.
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22

Камилов, Э. М., and А. А. Егоров. "3D Porous Structure Image Generation." Успехи кибернетики / Russian Journal of Cybernetics, no. 3(3) (September 30, 2020): 33–40. http://dx.doi.org/10.51790/2712-9942-2020-1-3-4.

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Разработана сверточная генеративно-состязательная нейронная сеть, генерирующая объемные изображения пористых сред (горной породы). Рассматриваются возможности модификации нейронной сети для генерации пористых сред с заданными характеристиками: коэффициент пористости, проницаемости, состав и размеры зерен, каналов и каверн. In this study, a convolutional generative adversarial neural network generating 3D images of porous media (rock) was developed. The neural network can be modified to generate porous media with specific properties such as porosity factor, permeability factor, composition and sizes of the grains, channels, and voids.
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23

Simon, Paul M., and Scott Graham. "Extending the Quality of Secure Service Model to Multi-Hop Networks." Journal of Cybersecurity and Privacy 1, no. 4 (December 15, 2021): 793–803. http://dx.doi.org/10.3390/jcp1040038.

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Rarely are communications networks point-to-point. In most cases, transceiver relay stations exist between transmitter and receiver end-points. These relay stations, while essential for controlling cost and adding flexibility to network architectures, reduce the overall security of the respective network. In an effort to quantify that reduction, we extend the Quality of Secure Service (QoSS) model to these complex networks, specifically multi-hop networks. In this approach, the quantification of security is based upon probabilities that adversarial listeners and disruptors gain access to or manipulate transmitted data on one or more of these multi-hop channels. Message fragmentation and duplication across available channels provides a security performance trade-space, with its consequent QoSS. This work explores that trade-space and the corresponding QoSS model to describe it.
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24

Liu, Qiao, Guang Gong, Yong Wang, and Hui Li. "A Novel Secure Transmission Scheme in MIMO Two-Way Relay Channels with Physical Layer Approach." Mobile Information Systems 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/7843843.

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Security issue has been considered as one of the most pivotal aspects for the fifth-generation mobile network (5G) due to the increasing demands of security service as well as the growing occurrence of security threat. In this paper, instead of focusing on the security architecture in the upper layer, we investigate the secure transmission for a basic channel model in a heterogeneous network, that is, two-way relay channels. By exploiting the properties of the transmission medium in the physical layer, we propose a novel secure scheme for the aforementioned channel mode. With precoding design, the proposed scheme is able to achieve a high transmission efficiency as well as security. Two different approaches have been introduced: information theoretical approach and physical layer encryption approach. We show that our scheme is secure under three different adversarial models: (1) untrusted relay attack model, (2) trusted relay with eavesdropper attack model, and (3) untrusted relay with eavesdroppers attack model. We also derive the secrecy capacity of the two different approaches under the three attacks. Finally, we conduct three simulations of our proposed scheme. The simulation results agree with the theoretical analysis illustrating that our proposed scheme could achieve a better performance than the existing schemes.
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25

Zhao, Wenxuan, Yaqin Zhao, Liqi Feng, and Jiaxi Tang. "Attention Optimized Deep Generative Adversarial Network for Removing Uneven Dense Haze." Symmetry 14, no. 1 (December 21, 2021): 1. http://dx.doi.org/10.3390/sym14010001.

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The existing dehazing algorithms are problematic because of dense haze being unevenly distributed on the images, and the deep convolutional dehazing network relying too greatly on large-scale datasets. To solve these problems, this paper proposes a generative adversarial network based on the deep symmetric Encoder-Decoder architecture for removing dense haze. To restore the clear image, a four-layer down-sampling encoder is constructed to extract the semantic information lost due to the dense haze. At the same time, in the symmetric decoder module, an attention mechanism is introduced to adaptively assign weights to different pixels and channels, so as to deal with the uneven distribution of haze. Finally, the framework of the generative adversarial network is generated so that the model achieves a better training effect on small-scale datasets. The experimental results showed that the proposed dehazing network can not only effectively remove the unevenly distributed dense haze in the real scene image, but also achieve great performance in real-scene datasets with less training samples, and the evaluation indexes are better than other widely used contrast algorithms.
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26

Boche, Holger, Minglai Cai, Janis Nötzel, and Christian Deppe. "Secret message transmission over quantum channels under adversarial quantum noise: Secrecy capacity and super-activation." Journal of Mathematical Physics 60, no. 6 (June 2019): 062202. http://dx.doi.org/10.1063/1.5019461.

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27

Qiao, Junbo, Xing Wang, Ji Chen, and Muwei Jian. "Low-Light Image Enhancement with an Anti-Attention Block-Based Generative Adversarial Network." Electronics 11, no. 10 (May 19, 2022): 1627. http://dx.doi.org/10.3390/electronics11101627.

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High-quality images are difficult to obtain in complex environments, such as underground or underwater. The low performance of images that are captured under low-light conditions significantly restricts the development of various engineering applications. However, existing algorithms exhibit color distortion or under/overexposure when addressing non-uniform illumination images. Furthermore, they introduce high-level noise when processing extremely dark images. In this paper, we propose a novel generative adversarial network (GAN) structure to generate high-quality enhanced images, which is called anti-attention block (AAB)-based generative adversarial networks (AABGAN). Specifically, we propose AAB to suppress undesired chromatic aberrations and establish a mapping relationship between different channels. The deep aggregation pyramid pooling module guides the network when combining multi-scale context information. Furthermore, we design a new multiple loss function to adjust images to the most suitable range for human vision. The results of extensive experiments show that our method outperforms state-of-the-art unsupervised image enhancement methods in terms of noise reduction and has a well-perceived result.
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28

Zhang, Jianfu, Yuanyuan Huang, Yaoyi Li, Weijie Zhao, and Liqing Zhang. "Multi-Attribute Transfer via Disentangled Representation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9195–202. http://dx.doi.org/10.1609/aaai.v33i01.33019195.

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Recent studies show significant progress in image-to-image translation task, especially facilitated by Generative Adversarial Networks. They can synthesize highly realistic images and alter the attribute labels for the images. However, these works employ attribute vectors to specify the target domain which diminishes image-level attribute diversity. In this paper, we propose a novel model formulating disentangled representations by projecting images to latent units, grouped feature channels of Convolutional Neural Network, to disassemble the information between different attributes. Thanks to disentangled representation, we can transfer attributes according to the attribute labels and moreover retain the diversity beyond the labels, namely, the styles inside each image. This is achieved by specifying some attributes and swapping the corresponding latent units to “swap” the attributes appearance, or applying channel-wise interpolation to blend different attributes. To verify the motivation of our proposed model, we train and evaluate our model on face dataset CelebA. Furthermore, the evaluation of another facial expression dataset RaFD demonstrates the generalizability of our proposed model.
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Liu, Changhong, Hongyin Li, Zhongwei Liang, Yongjun Zhang, Yier Yan, Ray Y. Zhong, and Shaohu Peng. "A Novel Deep-Learning-Based Enhanced Texture Transformer Network for Reference Image Super-Resolution." Electronics 11, no. 19 (September 24, 2022): 3038. http://dx.doi.org/10.3390/electronics11193038.

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The study explored a deep learning image super-resolution approach which is commonly used in face recognition, video perception and other fields. These generative adversarial networks usually have high-frequency texture details. The relevant textures of high-resolution images could be transferred as reference images to low-resolution images. The latest existing methods use transformer ideas to transfer related textures to low-resolution images, but there are still some problems with channel learning and detailed textures. Therefore, the study proposed an enhanced texture transformer network (ETTN) to improve the channel learning ability and details of the texture. It could learn the corresponding structural information of high-resolution texture images and convert it into low-resolution texture images. Through this, finding the feature map can change the exact feature of images and improve the learning ability between channels. We then used multi-scale feature integration (MSFI) to further enhance the effect of fusion and achieved different degrees of texture restoration. The experimental results show that the model has a good resolution enhancement effect on texture transformers. In different datasets, the peak signal to noise ratio (PSNR) and structural similarity (SSIM) were improved by 0.1–0.5 dB and 0.02, respectively.
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Salomon, Dor, Amir Weiss, and Itamar Levi. "Improved Filtering Techniques for Single- and Multi-Trace Side-Channel Analysis." Cryptography 5, no. 3 (September 13, 2021): 24. http://dx.doi.org/10.3390/cryptography5030024.

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Side-channel analysis (SCA) attacks constantly improve and evolve. Implementations are therefore designed to withstand strong SCA adversaries. Different side channels exhibit varying statistical characteristics of the sensed or exfiltrated leakage, as well as the embedding of different countermeasures. This makes it crucial to improve and adapt pre-processing and denoising techniques, and abilities to evaluate the adversarial best-case scenario. We address two popular SCA scenarios: (1) a single-trace context, modeling an adversary that captures only one leakage trace, and (2) a multi-trace (or statistical) scenario, that models the classical SCA context. Given that horizontal attacks, localized electromagnetic attacks and remote-SCA attacks are becoming evermore powerful, both scenarios are of interest and importance. In the single-trace context, we improve on existing Singular Spectral Analysis (SSA) based techniques by utilizing spectral property variations over time that stem from the cryptographic implementation. By adapting overlapped-SSA and optimizing over the method parameters, we achieve a significantly shorter computation time, which is the main challenge of the SSA-based technique, and a higher information gain (in terms of the Signal-to-Noise Ratio (SNR)). In the multi-trace context, a profiling strategy is proposed to optimize a Band-Pass Filter (BPF) based on a low-computational cost criterion, which is shown to be efficient for unprotected and low protection level countermeasures. In addition, a slightly more computationally intensive optimized ‘shaped’ filter is presented that utilizes a frequency-domain SNR-based coefficient thresholding. Our experimental results exhibit significant improvements over a set of various implementations embedded with countermeasures in hardware and software platforms, corresponding to varying baseline SNR levels and statistical leakage characteristics.
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31

Kaplunov, A. I. "Modern Approaches to Understanding the Administrative Process as a Result and the Basis for the Development of Domestic Administrative Procedural Legislation." Siberian Law Review 18, no. 3 (October 21, 2021): 261–76. http://dx.doi.org/10.19073/2658-7602-2021-18-3-261-276.

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The article provides an overview and analysis of modern approaches to understanding the administrative process as a sectoral type of legal process that have developed in domestic theory, taking into account the changes that have occurred in the procedural legislation of the Russian Federation over the past three decades after the collapse of the USSR in 1991. The process is classified as follows: complex on a jurisdictional basis; integrative; complex on the basis of managerial, judicial. Particular attention is paid to the critical analysis of the judicial approach to understanding the administrative process, the reasons for the disagreements of its supporters, firstly, with representatives of the science of civil procedural law regarding the determination of the procedural nature of administrative proceedings, and, secondly, with specialists in administrative law regarding the denial of the presence of administrative-procedural forms of activity of subjects of public administration and attempts thereby to disavow the domestic doctrine of the administrative process. The methodology for studying the nature of procedural activity is based on the analysis of the sectoral subject of legal regulation and three types of a unified method of substantive regulation (civil, administrative and criminal), the implementation channels of which are varieties of legal process in the form of civil, administrative and criminal process which are based on an adversarial or investigative type of jurisdictional process, or a law-granting type of legal process. This methodological approach made it possible: 1) to establish the sectoral procedural nature of administrative proceedings, which is determined not by the subject of a “dispute about law”, but by the method of legal regulation, represented by the civil law type of regulation of public relations, the implementation channel of which is an adversarial type of jurisdictional legal process, which is its nature as a civil process; 2) to identify the shortcomings of the model of administrative proceedings enshrined in Russian legislation, the essence of which is that an adversarial type of jurisdictional process intended for judicial protection of a person who has suffered from the actions of an official and, acting as a plaintiff in the case, is applied to persons who have violated the established prohibitions and restrictions, or committed administrative offenses and acting in the case as a defendant; 3) to substantiate the presence in the structure of the administrative process of procedural forms of activity of subjects of public administration as a channel for the implementation of the administrative-legal type of regulation of public relations and determine the list of administrative proceedings.
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Kang, Wei. "Application of Multi-Measurement Vector Based on the Wireless Sensor Network in Mechanical Fault Diagnosis." Mathematical Problems in Engineering 2022 (September 7, 2022): 1–7. http://dx.doi.org/10.1155/2022/2390119.

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In order to solve the problem of low positioning accuracy of mechanical fault diagnosis, a polarization GPR imaging reconstruction algorithm based on the MMV model was proposed. The algorithm was mainly based on the joint processing of the measured data of multiple polarization channels to achieve the reconstruction of the reflectance of the detection scene corresponding to each polarization channel. The simulation data processing results based on FDTD showed that compared with the traditional SMV model polarization imaging algorithm, the proposed imaging algorithm could improve the accuracy of target location reconstruction and the ability of background clutter suppression significantly. Compared with the SMV model, TCR obtained by the MMV model increased by 30%. As for the imaging results at different noise ratios, TCR obtained by the MMV model was 10% higher than that obtained by the SMV model. And when the ratio of available real data samples decreased to 25%, the sample data generation based on the adversarial generation network could greatly improve the classification accuracy of the fault diagnosis model. It could realize the detection of the target better, so as to locate faults accurately.
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Illarionova, Svetlana, Dmitrii Shadrin, Alexey Trekin, Vladimir Ignatiev, and Ivan Oseledets. "Generation of the NIR Spectral Band for Satellite Images with Convolutional Neural Networks." Sensors 21, no. 16 (August 21, 2021): 5646. http://dx.doi.org/10.3390/s21165646.

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The near-infrared (NIR) spectral range (from 780 to 2500 nm) of the multispectral remote sensing imagery provides vital information for landcover classification, especially concerning vegetation assessment. Despite the usefulness of NIR, it does not always accomplish common RGB. Modern achievements in image processing via deep neural networks make it possible to generate artificial spectral information, for example, to solve the image colorization problem. In this research, we aim to investigate whether this approach can produce not only visually similar images but also an artificial spectral band that can improve the performance of computer vision algorithms for solving remote sensing tasks. We study the use of a generative adversarial network (GAN) approach in the task of the NIR band generation using only RGB channels of high-resolution satellite imagery. We evaluate the impact of a generated channel on the model performance to solve the forest segmentation task. Our results show an increase in model accuracy when using generated NIR compared to the baseline model, which uses only RGB (0.947 and 0.914 F1-scores, respectively). The presented study shows the advantages of generating the extra band such as the opportunity to reduce the required amount of labeled data.
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Xiong, Quan, Liping Di, Quanlong Feng, Diyou Liu, Wei Liu, Xuli Zan, Lin Zhang, et al. "Deriving Non-Cloud Contaminated Sentinel-2 Images with RGB and Near-Infrared Bands from Sentinel-1 Images Based on a Conditional Generative Adversarial Network." Remote Sensing 13, no. 8 (April 14, 2021): 1512. http://dx.doi.org/10.3390/rs13081512.

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Sentinel-2 images have been widely used in studying land surface phenomena and processes, but they inevitably suffer from cloud contamination. To solve this critical optical data availability issue, it is ideal to fuse Sentinel-1 and Sentinel-2 images to create fused, cloud-free Sentinel-2-like images for facilitating land surface applications. In this paper, we propose a new data fusion model, the Multi-channels Conditional Generative Adversarial Network (MCcGAN), based on the conditional generative adversarial network, which is able to convert images from Domain A to Domain B. With the model, we were able to generate fused, cloud-free Sentinel-2-like images for a target date by using a pair of reference Sentinel-1/Sentinel-2 images and target-date Sentinel-1 images as inputs. In order to demonstrate the superiority of our method, we also compared it with other state-of-the-art methods using the same data. To make the evaluation more objective and reliable, we calculated the root-mean-square-error (RSME), R2, Kling–Gupta efficiency (KGE), structural similarity index (SSIM), spectral angle mapper (SAM), and peak signal-to-noise ratio (PSNR) of the simulated Sentinel-2 images generated by different methods. The results show that the simulated Sentinel-2 images generated by the MCcGAN have a higher quality and accuracy than those produced via the previous methods.
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Wails, Ryan, Andrew Stange, Eliana Troper, Aylin Caliskan, Roger Dingledine, Rob Jansen, and Micah Sherr. "Learning to Behave: Improving Covert Channel Security with Behavior-Based Designs." Proceedings on Privacy Enhancing Technologies 2022, no. 3 (July 2022): 179–99. http://dx.doi.org/10.56553/popets-2022-0068.

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Censorship-resistant communication systems generally use real-world cover protocols to establish a covert channel through which uncensored communication can occur. Unfortunately, many previously proposed systems use cover protocols inconsistently with the way humans normally use those protocols, leading to anomalous network traffic patterns that have been shown to be discoverable by real-world censors. In this paper, we argue that censorship-resistant communication systems should follow two behavior-based design properties: (i) behavioral independence: systems should isolate the operation of their covert channels from the operation of their cover protocols, and (ii) behavioral realism: systems should either opportunistically use existing genuine cover protocol instances or run new protocol instances that are modeled after genuine ones. These properties ensure that the behavior of a system’s users will not degrade its security. We demonstrate how to achieve these properties through the design and evaluation of Raven, a censorship-resistant messaging system that uses email cover protocols identically to the way humans use email. Raven uses a generative adversarial network that is trained on genuine email data to control the timing and sizes of the email messages it sends and receives, and these messages are transferred independently of user actions. Our evaluation shows that, compared to the state-of-the-art email-based Mailet system, Raven raises the false-positive rate from 3% to 50% when detecting covert channel usage with 100% recall.
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Wieslander, Håkan, Ankit Gupta, Ebba Bergman, Erik Hallström, and Philip John Harrison. "Learning to see colours: Biologically relevant virtual staining for adipocyte cell images." PLOS ONE 16, no. 10 (October 15, 2021): e0258546. http://dx.doi.org/10.1371/journal.pone.0258546.

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Fluorescence microscopy, which visualizes cellular components with fluorescent stains, is an invaluable method in image cytometry. From these images various cellular features can be extracted. Together these features form phenotypes that can be used to determine effective drug therapies, such as those based on nanomedicines. Unfortunately, fluorescence microscopy is time-consuming, expensive, labour intensive, and toxic to the cells. Bright-field images lack these downsides but also lack the clear contrast of the cellular components and hence are difficult to use for downstream analysis. Generating the fluorescence images directly from bright-field images using virtual staining (also known as “label-free prediction” and “in-silico labeling”) can get the best of both worlds, but can be very challenging to do for poorly visible cellular structures in the bright-field images. To tackle this problem deep learning models were explored to learn the mapping between bright-field and fluorescence images for adipocyte cell images. The models were tailored for each imaging channel, paying particular attention to the various challenges in each case, and those with the highest fidelity in extracted cell-level features were selected. The solutions included utilizing privileged information for the nuclear channel, and using image gradient information and adversarial training for the lipids channel. The former resulted in better morphological and count features and the latter resulted in more faithfully captured defects in the lipids, which are key features required for downstream analysis of these channels.
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Lawrance, Bendict, Harim Lee, Eunsu Park, Il-Hyun Cho, Yong-Jae Moon, Jin-Yi Lee, Shanmugaraju A, and Sumiaya Rahman. "Generation of Solar Coronal White-light Images from SDO/AIA EUV Images by Deep Learning." Astrophysical Journal 937, no. 2 (October 1, 2022): 111. http://dx.doi.org/10.3847/1538-4357/ac8c24.

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Abstract Low coronal white-light observations are very important to understand low coronal features of the Sun, but they are rarely made. We generate Mauna Loa Solar Observatory (MLSO) K-coronagraph like white-light images from the Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA) EUV images using a deep learning model based on conditional generative adversarial networks. In this study, we used pairs of SDO/AIA EUV (171, 193, and 211 Å) images and their corresponding MLSO K-coronagraph images between 1.11 and 1.25 solar radii from 2014 to 2019 (January to September) to train the model. For this we made seven (three using single channels and four using multiple channels) deep learning models for image translation. We evaluate the models by comparing the pairs of target white-light images and those of corresponding artificial intelligence (AI)–generated ones in October and November. Our results from the study are summarized as follows. First, the multiple channel AIA 193 and 211 Å model is the best among the seven models in view of the correlation coefficient (CC = 0.938). Second, the major low coronal features like helmet streamers, pseudostreamers, and polar coronal holes are well identified in the AI-generated ones by this model. The positions and sizes of the polar coronal holes of the AI-generated images are very consistent with those of the target ones. Third, from AI-generated images we successfully identified a few interesting solar eruptions such as major coronal mass ejections and jets. We hope that our model provides us with complementary data to study the low coronal features in white light, especially for nonobservable cases (during nighttime, poor atmospheric conditions, and instrumental maintenance).
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Jiang, Xin, Chunlei Zhao, Ming Zhu, Zhicheng Hao, and Wen Gao. "Residual Spatial and Channel Attention Networks for Single Image Dehazing." Sensors 21, no. 23 (November 27, 2021): 7922. http://dx.doi.org/10.3390/s21237922.

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Single image dehazing is a highly challenging ill-posed problem. Existing methods including both prior-based and learning-based heavily rely on the conceptual simplified atmospheric scattering model by estimating the so-called medium transmission map and atmospheric light. However, the formation of haze in the real world is much more complicated and inaccurate estimations further degrade the dehazing performance with color distortion, artifacts and insufficient haze removal. Moreover, most dehazing networks treat spatial-wise and channel-wise features equally, but haze is practically unevenly distributed across an image, thus regions with different haze concentrations require different attentions. To solve these problems, we propose an end-to-end trainable densely connected residual spatial and channel attention network based on the conditional generative adversarial framework to directly restore a haze-free image from an input hazy image, without explicitly estimation of any atmospheric scattering parameters. Specifically, a novel residual attention module is proposed by combining spatial attention and channel attention mechanism, which could adaptively recalibrate spatial-wise and channel-wise feature weights by considering interdependencies among spatial and channel information. Such a mechanism allows the network to concentrate on more useful pixels and channels. Meanwhile, the dense network can maximize the information flow along features from different levels to encourage feature reuse and strengthen feature propagation. In addition, the network is trained with a multi-loss function, in which contrastive loss and registration loss are novel refined to restore sharper structures and ensure better visual quality. Experimental results demonstrate that the proposed method achieves the state-of-the-art performance on both public synthetic datasets and real-world images with more visually pleasing dehazed results.
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Liang, Youwei, and Dong Huang. "Large Norms of CNN Layers Do Not Hurt Adversarial Robustness." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 8565–73. http://dx.doi.org/10.1609/aaai.v35i10.17039.

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Since the Lipschitz properties of convolutional neural networks (CNNs) are widely considered to be related to adversarial robustness, we theoretically characterize the L-1 norm and L-infinity norm of 2D multi-channel convolutional layers and provide efficient methods to compute the exact L-1 norm and L-infinity norm. Based on our theorem, we propose a novel regularization method termed norm decay, which can effectively reduce the norms of convolutional layers and fully-connected layers. Experiments show that norm-regularization methods, including norm decay, weight decay, and singular value clipping, can improve generalization of CNNs. However, they can slightly hurt adversarial robustness. Observing this unexpected phenomenon, we compute the norms of layers in the CNNs trained with three different adversarial training frameworks and surprisingly find that adversarially robust CNNs have comparable or even larger layer norms than their non-adversarially robust counterparts. Furthermore, we prove that under a mild assumption, adversarially robust classifiers can be achieved using neural networks, and an adversarially robust neural network can have an arbitrarily large Lipschitz constant. For this reason, enforcing small norms on CNN layers may be neither necessary nor effective in achieving adversarial robustness. The code is available at https://github.com/youweiliang/norm_robustness.
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Amandong, Egute. "Alternative Dispute Resolution (ADR) Hybrid in Cameroon as a Form of Legal Protection for Consumers of Defective Products." Brawijaya Law Journal 8, no. 1 (April 30, 2021): 54–69. http://dx.doi.org/10.21776/ub.blj.2021.008.01.04.

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As part of the awareness creation exercise, this paper seeks to establish that consumers of defective products in Cameroon should be exposed to the various extra-judicial channels once they can be adopted by Government and through which the consumers can enforce their rights. This is known as Alternative Dispute Resolution (ADR). The acronym ADR is a group of flexible approaches which could be applied in resolving disputes related to defective products more quickly and at a lower cost than going through the tedious road of adversarial proceedings. ADR mechanisms generally are intended to mean alternatives to the traditional court process. Their adoption will involve the use of impartial interveners who are referred to as “third parties” or “neutrals”. On the whole, the choice of a consumer redress mechanism is a choice between judicial and non-judicial mechanisms. The paper argues that, considering the difficulties encountered by the consumer within the adversarial system, the non-judicial mechanisms are more impactful and satisfactory to consumers than the judicial. It is equally argued that the judicial mechanisms depict a certain level of risk taking, that is, the risk of winning or losing and hence going without a remedy. This risk factor is much lower in the non or extra – judicial system or mechanism which reveals that in appropriate circumstances, the producers using the good customer relation basis, are minded to compensate even where the consumer’s claim is baseless. In this wise, it is therefore necessary to encourage the utilization of the extra - judicial mechanisms in resolving consumer complaints. Expediency, speed and low cost no doubt support this call.
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Goebel, Michael, Lakshmanan Nataraj, Tejaswi Nanjundaswamy, Tajuddin Manhar Mohammed, Shivkumar Chandrasekaran, and B. S. Manjunath. "Detection, Attribution and Localization of GAN Generated Images." Electronic Imaging 2021, no. 4 (January 18, 2021): 276–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.4.mwsf-276.

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Recent advances in Generative Adversarial Networks (GANs) have led to the creation of realistic-looking digital images that pose a major challenge to their detection by humans or computers. GANs are used in a wide range of tasks, from modifying small attributes of an image (StarGAN [14]), transferring attributes between image pairs (CycleGAN [92]), as well as generating entirely new images (ProGAN [37], StyleGAN [38], SPADE/GauGAN [65]). In this paper, we propose a novel approach to detect, attribute and localize GAN generated images that combines image features with deep learning methods. For every image, co-occurrence matrices are computed on neighborhood pixels of RGB channels in different directions (horizontal, vertical and diagonal). A deep learning network is then trained on these features to detect, attribute and localize these GAN generated/manipulated images. A large scale evaluation of our approach on 5 GAN datasets comprising over 2.76 million images (ProGAN, StarGAN, CycleGAN, StyleGAN and SPADE/GauGAN) shows promising results in detecting GAN generated images.
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42

Poterek, Q., P. A. Herrault, G. Forestier, and D. Schwartz. "REVEALING LONG-TERM PHYSIOLOGICAL TRAJECTORIES OF GRASSLANDS FROM LEGACY B&W AERIAL PHOTOGRAPHS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 549–57. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-549-2020.

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Abstract. Landscape reconstruction is crucial to measure the effects of climate change or past land use on current biodiversity. In particular, retracing past phenological changes can serve as a basis for explaining current patterns of plant communities and predict the future extinction of species. Old spatial data are currently used to reconstruct vegetation changes, both morphologically (with landscape metrics) and semantically (grasslands to crops for instance). However, poor radiometric properties (single panchromatic channel, illumination variation, etc.) do not offer the possibility to compute environmental variables (e.g. NDVI and color indices), which strongly limits long-term phenological reconstruction. In this study, we propose a workflow for reconstructing phenological trajectories of grasslands from 1958 to 2011, in the French central Vosges, from old aerial black and white (B&W) photographs. Noise and vignetting corruptions were first corrected in B&W photographs with non-local filtering algorithms. Panchromatic scans were then colorized with a Generative Adversarial Network (GAN). Based on the predicted channels, we finally computed digital greenness metrics (Green Chromatic Coordinate, Excess Greenness) to measure vegetation activity in grasslands. Our results demonstrated the feasibility of reconstructing long-term phenological trajectories from legacy photographs with insights at different levels: (1) the proposed correction methods provided radiometric improvements in old aerial missions; (2) the colorization process led to promising and plausible colorized historical products; (3) digital greenness metrics were useful for describing past vegetation activity.
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Khaleghi, Nastaran, Tohid Yousefi Rezaii, Soosan Beheshti, Saeed Meshgini, Sobhan Sheykhivand, and Sebelan Danishvar. "Visual Saliency and Image Reconstruction from EEG Signals via an Effective Geometric Deep Network-Based Generative Adversarial Network." Electronics 11, no. 21 (November 7, 2022): 3637. http://dx.doi.org/10.3390/electronics11213637.

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Reaching out the function of the brain in perceiving input data from the outside world is one of the great targets of neuroscience. Neural decoding helps us to model the connection between brain activities and the visual stimulation. The reconstruction of images from brain activity can be achieved through this modelling. Recent studies have shown that brain activity is impressed by visual saliency, the important parts of an image stimuli. In this paper, a deep model is proposed to reconstruct the image stimuli from electroencephalogram (EEG) recordings via visual saliency. To this end, the proposed geometric deep network-based generative adversarial network (GDN-GAN) is trained to map the EEG signals to the visual saliency maps corresponding to each image. The first part of the proposed GDN-GAN consists of Chebyshev graph convolutional layers. The input of the GDN part of the proposed network is the functional connectivity-based graph representation of the EEG channels. The output of the GDN is imposed to the GAN part of the proposed network to reconstruct the image saliency. The proposed GDN-GAN is trained using the Google Colaboratory Pro platform. The saliency metrics validate the viability and efficiency of the proposed saliency reconstruction network. The weights of the trained network are used as initial weights to reconstruct the grayscale image stimuli. The proposed network realizes the image reconstruction from EEG signals.
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Kalbantner, Jan, Konstantinos Markantonakis, Darren Hurley-Smith, Raja Naeem Akram, and Benjamin Semal. "P2PEdge: A Decentralised, Scalable P2P Architecture for Energy Trading in Real-Time." Energies 14, no. 3 (January 25, 2021): 606. http://dx.doi.org/10.3390/en14030606.

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Current Peer-to-Peer (P2P) energy market models raise serious concerns regarding the confidentiality and integrity of energy consumption, trading and billing data. While Distributed Ledger Technology (DLT) systems (e.g., blockchain) have been proposed to enhance security, an attacker could damage other parts of the model, such as its infrastructure: an adversarial attacker could target the communication between entities by, e.g., eavesdropping or modifying data. The main goal of this paper is to propose a model for a decentralised P2P marketplace for trading energy, which addresses the problem of developing security and privacy-aware environments. Additionally, a Multi-Agent System (MAS) architecture is presented with a focus on security and sustainability. In order to propose a solution to DLT’s scalability issues (i.e., through transaction confirmation delays), off-chain state channels are considered for the energy negotiation and resolution processes. Additionally, a STRIDE (spoofing, tampering, repudiation, information disclosure, denial of service, elevation of privilege) security analysis is conducted within the context of the proposed model to identify potential vulnerabilities.
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45

Shi, Lei. "Application Model Construction of Traditional Cultural Elements in Illustration Design under Artificial Intelligence Background." Mobile Information Systems 2022 (June 14, 2022): 1–9. http://dx.doi.org/10.1155/2022/7412066.

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The sustainable development of modern illustration art needs to dig deep into traditional culture in content and, on the basis of spreading cultural forms and improving cultural influence, supplement its own creative forms. The influence of AI (artificial intelligence) on illustration design not only is manifested in the optimization of illustration design tools and the improvement of design efficiency, but also makes the illustration design methods more diverse and promotes new breakthroughs in the illustration design concept under the influence of new technologies. In this paper, combining the advantages of two activation functions, SoftSign and ReLU, a new activation function, SReLU, is constructed and applied to CNN (Convolutional Neural Network). The multimode property of color prediction is modeled by quantifying ab color channels in Lab color space, and the introduction of GAN (Generative Adversarial Network) structure can make the whole model show better generation effect and improve the generation ability of image style conversion network. Results. The experiment proves the effectiveness of the method with high-quality image visual effect.
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Lu, Xiaoquan, Yu Zhou, Zhongdong Wang, Yongxian Yi, Longji Feng, and Fei Wang. "Knowledge Embedded Semi-Supervised Deep Learning for Detecting Non-Technical Losses in the Smart Grid." Energies 12, no. 18 (September 6, 2019): 3452. http://dx.doi.org/10.3390/en12183452.

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Non-technical losses (NTL) caused by fault or electricity theft is greatly harmful to the power grid. Industrial customers consume most of the power energy, and it is important to reduce this part of NTL. Currently, most work concentrates on analyzing characteristic of electricity consumption to detect NTL among residential customers. However, the related feature models cannot be adapted to industrial customers because they do not have a fixed electricity consumption pattern. Therefore, this paper starts from the principle of electricity measurement, and proposes a deep learning-based method to extract advanced features from massive smart meter data rather than artificial features. Firstly, we organize electricity magnitudes as one-dimensional sample data and embed the knowledge of electricity measurement in channels. Then, this paper proposes a semi-supervised deep learning model which uses a large number of unlabeled data and adversarial module to avoid overfitting. The experiment results show that our approach can achieve satisfactory performance even when trained by very small samples. Compared with the state-of-the-art methods, our method has achieved obvious improvement in all metrics.
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Shejole, Prof Sakshi. "Secret Communication Using Multi-Image Steganography and Face Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 1260–63. http://dx.doi.org/10.22214/ijraset.2022.47549.

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Abstract: Our Proposed system is to develop a Web Application for hiding information in any image file to ensure the safety of the exchange of data between different military parties and provide better security during message transmission. The scope of the project is the implementation of steganography tools for hiding information including any type of information file and image file and the path where the user wants to save the image and extruded file. We use the LSB technique. The proposed approach is to use a steganography algorithm for embedding data in the image files for military applications. For security purposes we used modules face Recognition technique with AES algorithm for Strong Security purpose. And we use the cover channel technique as an information hiding technique that can be exploited by a process to transfer information in a manner that violates the system security policies. And we use copyright marking techniques. In short, Cover Channels transfer information using nonstandard methods against the system design.d. Deep learning techniques used for image steganography can be broadly divided into three categories - traditional methods, Convolutional Neural Network-based and General Adversarial Network-based methods. Along with the methodology, an elaborate summary on the datasets used, experimental set-ups considered and the evaluation metrics commonly used are described in this paper. A table summarizing all the details are also provided for easy reference. This paper aims to help the fellow researchers by compiling the current trends, challenges and some future direction in this field.
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Meng Qi, 孟奇, 苗华 Miao Hua, 李琳 Li Lin, 国博 Guo Bo, 刘婷婷 Liu Tingting, and 米士隆 Mi Shilong. "基于双通道生成对抗网络的镜片缺陷数据增强." Laser & Optoelectronics Progress 58, no. 20 (2021): 2015001. http://dx.doi.org/10.3788/lop202158.2015001.

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49

Adelabu Salawu, Mashud Layiwola, and Simeon Abiodun Aina. "Education for Peace and Justice in Nigeria: A Critical Analysis 1999 – 2015." World Journal of Social Science 4, no. 1 (January 25, 2017): 40. http://dx.doi.org/10.5430/wjss.v4n1p40.

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The arrays of violent conflicts in Nigeria, and government’s reaction to them, through the application of adversarialhard powe, call for a review of governments, conflict handling styles. Since the advent of civilian administration in1999, education for peace and justice has not got the required impetus, and it should be at the bedrock of anydeveloping country’s master plan. The theory of pacifism, coined by the French peace campaigner, Emile Armand(2016), that peaceful, rather than violent or belligerent relations should govern human intercourse, was applied. Thispaper observed the prevalence of conflict in Nigeria, ranging from ethic and relations violence, Niger Delta crises,Boko Haram insurgency, communal conflicts, political violence, kidnapping, as well as the bombardment of courtswith political litigations among others. Lack of awareness of other non-adversarial methods of resolving conflicts hasled to its unabatedness, which has cost the country so much loss in human and material resources.This paper recommends that education peace and justice should be designed in a number of ways such as inworkshop and awareness campaigns. The formal channels must be well staffed with people grounded in peace andconflict studies, to be complemented with train-the trainers approach, in order to ensure suitable knowledge transfer.Government must exhibit good governance. As the level of illiteracy is high in the country, informal education forpeace and justice must be given greater emphasise. The use of internet and other means of information technologywill promote the dissemination of education for peace and justice in Nigeria.
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Hou Chunping, 侯春萍, 王霄聪 Wang Xiaocong, 夏晗 Xia Han, and 杨阳 Yang Yang. "基于双通路生成对抗网络的红外与可见光图像融合方法." Laser & Optoelectronics Progress 58, no. 14 (2021): 1410024. http://dx.doi.org/10.3788/lop202158.1410024.

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