Статті в журналах: "Deep learning for Multimedia Forensics"

1

Amerini, Irene, Aris Anagnostopoulos, Luca Maiano, and Lorenzo Ricciardi Celsi. "Deep Learning for Multimedia Forensics." Foundations and Trends® in Computer Graphics and Vision 12, no. 4 (2021): 309–457. http://dx.doi.org/10.1561/0600000096.

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2

.., Ossama, and Mhmed Algrnaodi. "Deep Learning Fusion for Attack Detection in Internet of Things Communications." Fusion: Practice and Applications 9, no. 2 (2022): 27–47. http://dx.doi.org/10.54216/fpa.090203.

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The increasing deep learning techniques used in multimedia and networkIoT solve many problems and increase performance. Securing the deep learning models, multimedia, and networkIoT has become a major area of research in the past few years which is considered to be a challenge during generative adversarial attacks over the multimedia or networkIoT. Many efforts and studies try to provide intelligent forensics techniques to solve security issues. This paper introduces a holistic organization of intelligent multimedia forensics that involve deep learning fusion, multimedia, and networkIoT forensics to attack detection. We highlight the importance of using deep learning fusion techniques to obtain intelligent forensics and security over multimedia or NetworkIoT. Finally, we discuss the key challenges and future directions in the area of intelligent multimedia forensics using deep learning fusion techniques.

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3

Celebi, Naciye Hafsa, Tze-Li Hsu, and Qingzhong Liu. "A comparison study to detect seam carving forgery in JPEG images with deep learning models." Journal of Surveillance, Security and Safety 3, no. 3 (2022): 88–100. http://dx.doi.org/10.20517/jsss.2022.02.

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Aim: Although deep learning has been applied in image forgery detection, to our knowledge, it still falls short of a comprehensive comparison study in detecting seam-carving images in multimedia forensics by comparing the popular deep learning models, which is addressed in this study. Methods: To investigate the performance in detecting seam-carving-based image forgery with popular deep learning models that were used in image forensics, we compared nine different deep learning models in detecting untouched JPEG images, seam-insertion images, and seam removal images (three-class classification), and in distinguishing modified seam-carving images from untouched JPEG images (binary classification). We also investigate the different learning algorithms with the Efficientnet-B5 in adjusting the learning rate with three popular optimizers in deep learning. Results: Our study shows that EfficientNet performs the best among the nine different deep learning frameworks, followed by SRnet, and LFnet. Different algorithms for adjusting the learning rate result in different detection testing accuracy with Efficientnet-B5. In our experiments, decouples the optimal choice of weight decay factor from the setting of the learning rate (AdamW) is generally superior to Adaptive Moment Estimation (Adam) and Stochastic Gradient Descent (SGD). Our study also indicates that deep learning is very promising for image forensics, such as the detection of image forgery. Conclusion: Deep learning is very promising in image forensics that is hardly discernable to human perceptions, but the performance varies over different learning models and frameworks. In addition to the models, the optimizer has a considerable impact on the final detection performance. We would recommend EfficientNet, LFnet and SRnet for seam-carving detection.

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Hussain, Israr, Dostdar Hussain, Rashi Kohli, Muhammad Ismail, Saddam Hussain, Syed Sajid Ullah, Roobaea Alroobaea, Wajid Ali, and Fazlullah Umar. "Evaluation of Deep Learning and Conventional Approaches for Image Recaptured Detection in Multimedia Forensics." Mobile Information Systems 2022 (June 15, 2022): 1–10. http://dx.doi.org/10.1155/2022/2847580.

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Анотація:

Image recaptured from a high-resolution LED screen or a good quality printer is difficult to distinguish from its original counterpart. The forensic community paid less attention to this type of forgery than to other image alterations such as splicing, copy-move, removal, or image retouching. It is significant to develop secure and automatic techniques to distinguish real and recaptured images without prior knowledge. Image manipulation traces can be hidden using recaptured images. For this reason, being able to detect recapture images becomes a hot research topic for a forensic analyst. The attacker can recapture the manipulated images to fool image forensic system. As far as we know, there is no prior research that has examined the pros and cons of up-to-date image recaptured techniques. The main objective of this survey was to succinctly review the recent outcomes in the field of image recaptured detection and investigated the limitations in existing approaches and datasets. The outcome of this study provides several promising directions for further significant research on image recaptured detection. Finally, some of the challenges in the existing datasets and numerous promising directions on recaptured image detection are proposed to demonstrate how these difficulties might be carried into promising directions for future research. We also discussed the existing image recaptured datasets, their limitations, and dataset collection challenges.

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Premanand Ghadekar, Vaibhavi Shetty, Prapti Maheshwari, Raj Shah, Anish Shaha, and Vaishnav Sonawane. "Non-Facial Video Spatiotemporal Forensic Analysis Using Deep Learning Techniques." Proceedings of Engineering and Technology Innovation 23 (January 1, 2023): 01–14. http://dx.doi.org/10.46604/peti.2023.10290.

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Digital content manipulation software is working as a boon for people to edit recorded video or audio content. To prevent the unethical use of such readily available altering tools, digital multimedia forensics is becoming increasingly important. Hence, this study aims to identify whether the video and audio of the given digital content are fake or real. For temporal video forgery detection, the convolutional 3D layers are used to build a model which can identify temporal forgeries with an average accuracy of 85% on the validation dataset. Also, the identification of audio forgery, using a ResNet-34 pre-trained model and the transfer learning approach, has been achieved. The proposed model achieves an accuracy of 99% with 0.3% validation loss on the validation part of the logical access dataset, which is better than earlier models in the range of 90-95% accuracy on the validation set.

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Ferreira, Sara, Mário Antunes, and Manuel E. Correia. "Exposing Manipulated Photos and Videos in Digital Forensics Analysis." Journal of Imaging 7, no. 7 (June 24, 2021): 102. http://dx.doi.org/10.3390/jimaging7070102.

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Tampered multimedia content is being increasingly used in a broad range of cybercrime activities. The spread of fake news, misinformation, digital kidnapping, and ransomware-related crimes are amongst the most recurrent crimes in which manipulated digital photos and videos are the perpetrating and disseminating medium. Criminal investigation has been challenged in applying machine learning techniques to automatically distinguish between fake and genuine seized photos and videos. Despite the pertinent need for manual validation, easy-to-use platforms for digital forensics are essential to automate and facilitate the detection of tampered content and to help criminal investigators with their work. This paper presents a machine learning Support Vector Machines (SVM) based method to distinguish between genuine and fake multimedia files, namely digital photos and videos, which may indicate the presence of deepfake content. The method was implemented in Python and integrated as new modules in the widely used digital forensics application Autopsy. The implemented approach extracts a set of simple features resulting from the application of a Discrete Fourier Transform (DFT) to digital photos and video frames. The model was evaluated with a large dataset of classified multimedia files containing both legitimate and fake photos and frames extracted from videos. Regarding deepfake detection in videos, the Celeb-DFv1 dataset was used, featuring 590 original videos collected from YouTube, and covering different subjects. The results obtained with the 5-fold cross-validation outperformed those SVM-based methods documented in the literature, by achieving an average F1-score of 99.53%, 79.55%, and 89.10%, respectively for photos, videos, and a mixture of both types of content. A benchmark with state-of-the-art methods was also done, by comparing the proposed SVM method with deep learning approaches, namely Convolutional Neural Networks (CNN). Despite CNN having outperformed the proposed DFT-SVM compound method, the competitiveness of the results attained by DFT-SVM and the substantially reduced processing time make it appropriate to be implemented and embedded into Autopsy modules, by predicting the level of fakeness calculated for each analyzed multimedia file.

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Parkhi, Abhinav, and Atish Khobragade. "Review on deep learning based techniques for person re-identification." 3C TIC: Cuadernos de desarrollo aplicados a las TIC 11, no. 2 (December 29, 2022): 208–23. http://dx.doi.org/10.17993/3ctic.2022.112.208-223.

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In-depth study has recently been concentrated on human re-identification, which is a crucial component of automated video surveillance. Re-identification is the act of identifying someone in photos or videos acquired from other cameras after they have already been recognized in an image or video from one camera. Re-identification, which involves generating consistent labelling between several cameras, or even just one camera, is required to reconnect missing or interrupted tracks. In addition to surveillance, it may be used in forensics, multimedia, and robotics.Re-identification of the person is a difficult problem since their look fluctuates across many cameras with visual ambiguity and spatiotemporal uncertainty. These issues can be largely caused by inadequate video feeds or lowresolution photos that are full of unnecessary facts and prevent re-identification. The geographical or temporal restrictions of the challenge are difficult to capture. The computer vision research community has given the problem a lot of attention because of how widely used and valuable it is. In this article, we look at the issue of human re-identification and discuss some viable approaches.

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8

Ferreira, Sara, Mário Antunes, and Manuel E. Correia. "A Dataset of Photos and Videos for Digital Forensics Analysis Using Machine Learning Processing." Data 6, no. 8 (August 5, 2021): 87. http://dx.doi.org/10.3390/data6080087.

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Deepfake and manipulated digital photos and videos are being increasingly used in a myriad of cybercrimes. Ransomware, the dissemination of fake news, and digital kidnapping-related crimes are the most recurrent, in which tampered multimedia content has been the primordial disseminating vehicle. Digital forensic analysis tools are being widely used by criminal investigations to automate the identification of digital evidence in seized electronic equipment. The number of files to be processed and the complexity of the crimes under analysis have highlighted the need to employ efficient digital forensics techniques grounded on state-of-the-art technologies. Machine Learning (ML) researchers have been challenged to apply techniques and methods to improve the automatic detection of manipulated multimedia content. However, the implementation of such methods have not yet been massively incorporated into digital forensic tools, mostly due to the lack of realistic and well-structured datasets of photos and videos. The diversity and richness of the datasets are crucial to benchmark the ML models and to evaluate their appropriateness to be applied in real-world digital forensics applications. An example is the development of third-party modules for the widely used Autopsy digital forensic application. This paper presents a dataset obtained by extracting a set of simple features from genuine and manipulated photos and videos, which are part of state-of-the-art existing datasets. The resulting dataset is balanced, and each entry comprises a label and a vector of numeric values corresponding to the features extracted through a Discrete Fourier Transform (DFT). The dataset is available in a GitHub repository, and the total amount of photos and video frames is 40,588 and 12,400, respectively. The dataset was validated and benchmarked with deep learning Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) methods; however, a plethora of other existing ones can be applied. Generically, the results show a better F1-score for CNN when comparing with SVM, both for photos and videos processing. CNN achieved an F1-score of 0.9968 and 0.8415 for photos and videos, respectively. Regarding SVM, the results obtained with 5-fold cross-validation are 0.9953 and 0.7955, respectively, for photos and videos processing. A set of methods written in Python is available for the researchers, namely to preprocess and extract the features from the original photos and videos files and to build the training and testing sets. Additional methods are also available to convert the original PKL files into CSV and TXT, which gives more flexibility for the ML researchers to use the dataset on existing ML frameworks and tools.

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Meshchaninov, Viacheslav Pavlovich, Ivan Andreevich Molodetskikh, Dmitriy Sergeevich Vatolin, and Alexey Gennadievich Voloboy. "Combining contrastive and supervised learning for video super-resolution detection." Keldysh Institute Preprints, no. 80 (2022): 1–13. http://dx.doi.org/10.20948/prepr-2022-80.

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Upscaled video detection is a helpful tool in multimedia forensics, but it’s a challenging task that involves various upscaling and compression algorithms. There are many resolution-enhancement methods, including interpolation and deep-learning based super-resolution, and they leave unique traces. This paper proposes a new upscaled-resolution-detection method based on learning of visual representations using contrastive and cross-entropy losses. To explain how the method detects videos, the major components of our framework are systematically reviewed — in particular, it is shown that most data-augmentation approaches hinder the learning of the method. Through extensive experiments on various datasets, our method has been shown to effectively detects upscaling even in compressed videos and outperforms the state-of-theart alternatives. The code and models are publicly available at https://github.com/msu-video-group/SRDM.

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10

Jiang, Jianguo, Boquan Li, Baole Wei, Gang Li, Chao Liu, Weiqing Huang, Meimei Li, and Min Yu. "FakeFilter: A cross-distribution Deepfake detection system with domain adaptation." Journal of Computer Security 29, no. 4 (June 18, 2021): 403–21. http://dx.doi.org/10.3233/jcs-200124.

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Abuse of face swap techniques poses serious threats to the integrity and authenticity of digital visual media. More alarmingly, fake images or videos created by deep learning technologies, also known as Deepfakes, are more realistic, high-quality, and reveal few tampering traces, which attracts great attention in digital multimedia forensics research. To address those threats imposed by Deepfakes, previous work attempted to classify real and fake faces by discriminative visual features, which is subjected to various objective conditions such as the angle or posture of a face. Differently, some research devises deep neural networks to discriminate Deepfakes at the microscopic-level semantics of images, which achieves promising results. Nevertheless, such methods show limited success as encountering unseen Deepfakes created with different methods from the training sets. Therefore, we propose a novel Deepfake detection system, named FakeFilter, in which we formulate the challenge of unseen Deepfake detection into a problem of cross-distribution data classification, and address the issue with a strategy of domain adaptation. By mapping different distributions of Deepfakes into similar features in a certain space, the detection system achieves comparable performance on both seen and unseen Deepfakes. Further evaluation and comparison results indicate that the challenge has been successfully addressed by FakeFilter.

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11

Dureja, Aman, and Payal Pahwa. "Image retrieval techniques: a survey." International Journal of Engineering & Technology 7, no. 1.2 (December 28, 2017): 215. http://dx.doi.org/10.14419/ijet.v7i1.2.9231.

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In the recent years, the development in computer technologies and multimedia applications has led to the production of huge digital images and large image databases, and it is increasing rapidly. There are several different areas in which image retrieval plays a crucial role like Medical systems, Forensic Labs, Tourism Promotion, etc. Thus retrieval of similar images is a challenge. To tackle this rapid growth in digital repositories it is necessary to develop image retrieval systems, which can operate on large databases. There are basically three techniques, which is useful for efficient retrieval of images. With these techniques, the number of methods has been modified for the efficient image retrieval of images. In this paper, we presented the survey of different techniques that has been used starting from Image retrieval using visual features and latest by the deep learning with CNN that contains the number of layers and now becomes the best base method for retrieval of images from the large databases. In the last section, we have made the analysis between various developed techniques and showed the advantages and disadvantages of various techniques.

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12

Chen, Shu-Ching. "Multimedia Deep Learning." IEEE MultiMedia 26, no. 1 (January 1, 2019): 5–7. http://dx.doi.org/10.1109/mmul.2019.2897471.

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13

Mainoo, Bernard. "Digital Multimedia Tampering Detection for Forensics Analysis." Advances in Multidisciplinary and scientific Research Journal Publication 1, no. 1 (July 22, 2022): 81–90. http://dx.doi.org/10.22624/aims/crp-bk3-p14.

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In the virtual multimedia era, digital forensics is turning into a rising location of studies way to the huge quantity of picture and video documents generated. Ensuring the integrity of such media is of top-notch significance in lots of situations. This mission has end up extra complex, particularly with the development of symmetrical and asymmetrical community systems which make their authenticity tough. Consequently, it's far definitely vital to find out all feasible modes of manipulation through the improvement of latest forensics detector tools. For example, the symmetry and asymmetry inconsistencies associated with visible characteristic residences are capacity while carried out at a couple of scales and locations. We discover right here this subject matter and advocate a comprehensible smooth taxonomy and a deep assessment of the brand-new studies regarding multimedia forgery detection. Then, an in-intensity dialogue and destiny guidelines for similarly research are provided. This painting gives a possibility for researchers to apprehend the cutting-edge lively subject and to assist them broaden and examine their very own picture/video forensics approaches. Keywords: Digital forensics, Multimedia tampering, Image/video processing, Watermarking, pattern recognition, Active/Passive Tampering Detection

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Dodoo, Herbert Cyril. "Deep Learning (DL) Oriented Forensic Analysis." Advances in Multidisciplinary and scientific Research Journal Publication 1, no. 1 (July 26, 2022): 321–28. http://dx.doi.org/10.22624/aims/crp-bk3-p51.

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Cyber-attacks are now more prevalent than ever before in all aspects of our daily lives. As a result of this circumstance, both individuals and organizations are fighting cybercrime on a regular basis. Furthermore, today's hackers have advanced a step further and are capable of employing complex cyber-attack strategies, exacerbating the problem. Some of these approaches are minute and undetectable, and they frequently masquerade as genuine requests and directives. To combat this threat, cyber security professionals, as well as digital forensic investigators, are constantly compelled to filter through massive and complicated pools of data, also known as Big Data, in order to uncover Potential Digital Forensic Evidence. that can be used as evidence in court. Potential Digital Evidence can then be used to assist investigators in reaching certain conclusions and/or judgments. The fact that Big Data frequently comes from various sources and has diverse file formats makes cyber forensics even more difficult for investigators. When it comes to the processing of vast amounts of complicated data for forensic purposes, forensic investigators typically have less time and budget to fulfil the rising demands. This paper will be studying how to incorporate Deep Learning cognitive computing approaches into Cyber Forensics Keywords: Deep Learning, Forensic Analysis, Artificial Intelligence, Online Safety, Evidence BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security & Forensics. Open Access. Distributed Free Citation: Herbert Cyril Dodoo (2022): Deep Learning (DL) Oriented Forensic Analysis Book Chapter Series on Research Nexus in IT, Law, Cyber Security & Forensics. Pp 320-328 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P51

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15

Chen, Shu-Ching. "Multimedia Meets Deep Reinforcement Learning." IEEE MultiMedia 29, no. 3 (July 1, 2022): 5–7. http://dx.doi.org/10.1109/mmul.2022.3196479.

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Zhang, Wei, Ting Yao, Shiai Zhu, and Abdulmotaleb El Saddik. "Deep Learning–Based Multimedia Analytics." ACM Transactions on Multimedia Computing, Communications, and Applications 15, no. 1s (February 23, 2019): 1–26. http://dx.doi.org/10.1145/3279952.

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17

Ota, Kaoru, Minh Son Dao, Vasileios Mezaris, and Francesco G. B. De Natale. "Deep Learning for Mobile Multimedia." ACM Transactions on Multimedia Computing, Communications, and Applications 13, no. 3s (August 10, 2017): 1–22. http://dx.doi.org/10.1145/3092831.

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18

Yang, Pengpeng, Daniele Baracchi, Rongrong Ni, Yao Zhao, Fabrizio Argenti, and Alessandro Piva. "A Survey of Deep Learning-Based Source Image Forensics." Journal of Imaging 6, no. 3 (March 4, 2020): 9. http://dx.doi.org/10.3390/jimaging6030009.

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Image source forensics is widely considered as one of the most effective ways to verify in a blind way digital image authenticity and integrity. In the last few years, many researchers have applied data-driven approaches to this task, inspired by the excellent performance obtained by those techniques on computer vision problems. In this survey, we present the most important data-driven algorithms that deal with the problem of image source forensics. To make order in this vast field, we have divided the area in five sub-topics: source camera identification, recaptured image forensic, computer graphics (CG) image forensic, GAN-generated image detection, and source social network identification. Moreover, we have included the works on anti-forensics and counter anti-forensics. For each of these tasks, we have highlighted advantages and limitations of the methods currently proposed in this promising and rich research field.

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ZHAO, Wei, Pengpeng YANG, Rongrong NI, Yao ZHAO, and Haorui WU. "Security Consideration for Deep Learning-Based Image Forensics." IEICE Transactions on Information and Systems E101.D, no. 12 (December 1, 2018): 3263–66. http://dx.doi.org/10.1587/transinf.2018edl8091.

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20

Ulloa, Carlos, Dora M. Ballesteros, and Diego Renza. "Video Forensics: Identifying Colorized Images Using Deep Learning." Applied Sciences 11, no. 2 (January 6, 2021): 476. http://dx.doi.org/10.3390/app11020476.

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In recent years there has been a significant increase in images and videos circulating in social networks and media, edited with different techniques, including colorization. This has a negative impact on the forensic field because it is increasingly difficult to discern what is original content and what is fake. To address this problem, we propose two models (a custom architecture and a transfer-learning-based model) based on CNNs that allows a fast recognition of the colorized images (or videos). In the experimental test, the effect of three hyperparameters on the performance of the classifier were analyzed in terms of HTER (Half Total Error Rate). The best result was found for the Adam optimizer, with a dropout of 0.25 and an input image size of 400 × 400 pixels. Additionally, the proposed models are compared with each other in terms of performance and inference times and with some state-of-the-art approaches. In terms of inference times per image, the proposed custom model is 12x faster than the transfer-learning-based model; however, in terms of precision (P), recall and F1-score, the transfer-learning-based model is better than the custom model. Both models generalize better than other models reported in the literature.

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Ekhande, Sonali, Uttam Patil, and Kshama Vishwanath Kulhalli. "Review on effectiveness of deep learning approach in digital forensics." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (October 1, 2022): 5481. http://dx.doi.org/10.11591/ijece.v12i5.pp5481-5592.

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Cyber forensics is use of scientific methods for definite description of cybercrime activities. It deals with collecting, processing and interpreting digital evidence for cybercrime analysis. Cyber forensic analysis plays very important role in criminal investigations. Although lot of research has been done in cyber forensics, it is still expected to face new challenges in near future. Analysis of digital media specifically photographic images, audio and video recordings are very crucial in forensics This paper specifically focus on digital forensics. There are several methods for digital forensic analysis. Currently deep learning (DL), mainly convolutional neural network (CNN) has proved very promising in classification of digital images and sound analysis techniques. This paper presents a compendious study of recent research and methods in forensic areas based on CNN, with a view to guide the researchers working in this area. We first, defined and explained preliminary models of DL. In the next section, out of several DL models we have focused on CNN and its usage in areas of digital forensic. Finally, conclusion and future work are discussed. The review shows that CNN has proved good in most of the forensic domains and still promise to be better.

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Bourouis, Sami, Roobaea Alroobaea, Abdullah M. Alharbi, Murad Andejany, and Saeed Rubaiee. "Recent Advances in Digital Multimedia Tampering Detection for Forensics Analysis." Symmetry 12, no. 11 (November 1, 2020): 1811. http://dx.doi.org/10.3390/sym12111811.

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Анотація:

In the digital multimedia era, digital forensics is becoming an emerging area of research thanks to the large amount of image and video files generated. Ensuring the integrity of such media is of great importance in many situations. This task has become more complex, especially with the progress of symmetrical and asymmetrical network structures which make their authenticity difficult. Consequently, it is absolutely imperative to discover all possible modes of manipulation through the development of new forensics detector tools. Although many solutions have been developed, tamper-detection performance is far from reliable and it leaves this problem widely open for further investigation. In particular, many types of multimedia fraud are difficult to detect because some evidences are not exploited. For example, the symmetry and asymmetry inconsistencies related to visual feature properties are potential when applied at multiple scales and locations. We explore here this topic and propose an understandable soft taxonomy and a deep overview of the latest research concerning multimedia forgery detection. Then, an in-depth discussion and future directions for further investigation are provided. This work offers an opportunity for researchers to understand the current active field and to help them develop and evaluate their own image/video forensics approaches.

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Carvalho, Tiago Jose de, Helio Pedrini, and Anderson De Rezende Rocha. "Visual Computing and Machine Learning Techniques for Digital Forensics." Revista de Informática Teórica e Aplicada 22, no. 1 (May 18, 2015): 128. http://dx.doi.org/10.22456/2175-2745.49492.

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It is impressive how fast science has improved day by day in so many different fields. In special, technology advances are shocking so many people bringing to their reality facts that previously were beyond their imagination. Inspired by methods earlier presented in scientific fiction shows, the computer science community has created a new research area named Digital Forensics, which aims at developing and deploying methods for fighting against digital crimes such as digital image forgery.This work presents some of the main concepts associated with Digital Forensics and, complementarily, presents some recent and powerful techniques relying on Computer Graphics, Image Processing, Computer Vision and Machine Learning concepts for detecting forgeries in photographs. Some topics addressed in this work include: sourceattribution, spoofing detection, pornography detection, multimedia phylogeny, and forgery detection. Finally, this work highlights the challenges and open problems in Digital Image Forensics to provide the readers with the myriad opportunities available for research.

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Chellappa, Rama, Diego Gragnaniello, Chang-Tsun Li, Francesco Marra, and Richa Singh. "Guest Editorial: Adversarial Deep Learning in Biometrics & Forensics." Computer Vision and Image Understanding 208-209 (July 2021): 103227. http://dx.doi.org/10.1016/j.cviu.2021.103227.

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25

Eom, So-Jeong, Hyun-Soo Kim, and Hae-Yeoun Lee. "Audio Forensics for Smartphone Recording Detection using Deep Learning." Journal of Korean Institute of Information Technology 20, no. 7 (July 31, 2022): 103–9. http://dx.doi.org/10.14801/jkiit.2022.20.7.103.

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Karie, Nickson M., Victor R. Kebande, and H. S. Venter. "Diverging deep learning cognitive computing techniques into cyber forensics." Forensic Science International: Synergy 1 (2019): 61–67. http://dx.doi.org/10.1016/j.fsisyn.2019.03.006.

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Gudnavar, Anand, Preetam Anvekar, Shraddha Sambrekar, and Tejashwini Pallakke. "Survey on Detection of Manipulated Multimedia in Digital Forensics Using Machine Learning." Journal of Cyber Security in Computer System 2, no. 1 (January 10, 2023): 13–17. http://dx.doi.org/10.46610/jcscs.2023.v02i01.002.

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Анотація:

The manipulation of multimedia has increased all over the world. Different tools are used to alter the multimedia and it is difficult to detect genuine and fake media. People are facing problems to detect if the media is real or fake. Due to manipulated media, cybercrime has becomeincreasingly widespread. We believe that personal security and privacy should be carried out easily and intelligently in this digital environment where all fundamental tasks are completed without issue. When we looked into the numbers, we discovered that a sizable proportion of people experience harassment or other forms of abuseregularly. Based on a review of the existing system, we presented an application that would use the CNN (Convolutional Neural Networks) method to distinguish between real and fraudulent media in a single application. CNN performs better with picture and voice or audio inputs than earlier networks and other techniques. CNN hidden extract feature from the input using pixels value and computation based on edges and outline of the inputs using pixels value and computation based on edges and outline of the inputs. The growing use of convolutional neural networks (CNNs) has had a substantial effect on defenders.

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Zhao, Xueyi, Xi Li, and Zhongfei Zhang. "Multimedia Retrieval via Deep Learning to Rank." IEEE Signal Processing Letters 22, no. 9 (September 2015): 1487–91. http://dx.doi.org/10.1109/lsp.2015.2410134.

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Qi, Guo-Jun, Hugo Larochelle, Benoit Huet, Jiebo Luo, and Kai Yu. "Guest Editorial: Deep Learning for Multimedia Computing." IEEE Transactions on Multimedia 17, no. 11 (November 2015): 1873–74. http://dx.doi.org/10.1109/tmm.2015.2485538.

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30

Qamhan, Mustafa A., Hamdi Altaheri, Ali Hamid Meftah, Ghulam Muhammad, and Yousef Ajami Alotaibi. "Digital Audio Forensics: Microphone and Environment Classification Using Deep Learning." IEEE Access 9 (2021): 62719–33. http://dx.doi.org/10.1109/access.2021.3073786.

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31

E, Sri Vishva. "A Review on Application of Deep Learning in Cyber Forensics." International Journal for Research in Applied Science and Engineering Technology 8, no. 7 (July 31, 2020): 61–62. http://dx.doi.org/10.22214/ijraset.2020.7011.

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32

Zhu, Xinshan, Yongjun Qian, Xianfeng Zhao, Biao Sun, and Ya Sun. "A deep learning approach to patch-based image inpainting forensics." Signal Processing: Image Communication 67 (September 2018): 90–99. http://dx.doi.org/10.1016/j.image.2018.05.015.

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33

Caldelli, Roberto, Marc Chaumont, Chang-Tsun Li, and Irene Amerini. "Special issue on Deep Learning in Image and Video Forensics." Signal Processing: Image Communication 75 (July 2019): 199–200. http://dx.doi.org/10.1016/j.image.2019.05.005.

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34

Thammaiah, Chethana Hadya, and Trisiladevi Chandrakant Nagavi. "An approach to partial occlusion using deep metric learning." International Journal of Informatics and Communication Technology (IJ-ICT) 10, no. 3 (December 1, 2021): 204. http://dx.doi.org/10.11591/ijict.v10i3.pp204-211.

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The human face can be used as an identification and authentication tool in biometric systems. Face recognition in forensics is a challenging task due to the presence of partial occlusion features like wearing a hat, sunglasses, scarf, and beard. In forensics, criminal identification having partial occlusion features is the most difficult task to perform. In this paper, a combination of the histogram of gradients (HOG) with Euclidean distance is proposed. Deep metric learning is the process of measuring the similarity between the samples using optimal distance metrics for learning tasks. In the proposed system, a deep metric learning technique like HOG is used to generate a 128d real feature vector. Euclidean distance is then applied between the feature vectors and a tolerance threshold is set to decide whether it is a match or mismatch. Experiments are carried out on disguised faces in the wild (DFW) dataset collected from IIIT Delhi which consists of 1000 subjects in which 600 subjects were used for testing and the remaining 400 subjects were used for training purposes. The proposed system provides a recognition accuracy of 89.8% and it outperforms compared with other existing methods.

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35

Satoh, Shin'ichi. "Multimedia Retrieval and the Impact of Deep Learning." Brain & Neural Networks 26, no. 4 (December 5, 2019): 117–22. http://dx.doi.org/10.3902/jnns.26.117.

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36

Hayat, Khizar. "Multimedia super-resolution via deep learning: A survey." Digital Signal Processing 81 (October 2018): 198–217. http://dx.doi.org/10.1016/j.dsp.2018.07.005.

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Li, Wei. "Multimedia Teaching of College Musical Education Based on Deep Learning." Mobile Information Systems 2021 (April 2, 2021): 1–10. http://dx.doi.org/10.1155/2021/5545470.

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In view of the current situation of musical education and the need for reform in China, we adopt two different methods, i.e., literature method and interview method in this research work. From these methods, we read a lot of musical education, multimedia technology, and modern teaching and reform. This research work is divided into two main phases. Firstly, the article mainly discusses the characteristics of college musical education compared with other cultural courses and the feasibility of multimedia technology and the auxiliary function of musical education that is applied in school’s musical education. Secondly, brain computing attempts to analyze things by simulating the structure and information processing of biological neural networks. The intelligent learning characteristic of a deep learning algorithm is proposed to monitor the process of musical education teaching and analyze the process quality. Finally, we introduced the design and production of network multimedia courseware which will help in theoretical guidance and reference to the application of multimedia technology in college musical education in China. Moreover, the outcome of the proposed model can play a role in solving and answering questions in the current multimedia application process and Chinese college music workers will apply multimedia technology more effectively and skillfully.

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Castillo Camacho, Ivan, and Kai Wang. "A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics." Journal of Imaging 7, no. 4 (April 3, 2021): 69. http://dx.doi.org/10.3390/jimaging7040069.

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Seeing is not believing anymore. Different techniques have brought to our fingertips the ability to modify an image. As the difficulty of using such techniques decreases, lowering the necessity of specialized knowledge has been the focus for companies who create and sell these tools. Furthermore, image forgeries are presently so realistic that it becomes difficult for the naked eye to differentiate between fake and real media. This can bring different problems, from misleading public opinion to the usage of doctored proof in court. For these reasons, it is important to have tools that can help us discern the truth. This paper presents a comprehensive literature review of the image forensics techniques with a special focus on deep-learning-based methods. In this review, we cover a broad range of image forensics problems including the detection of routine image manipulations, detection of intentional image falsifications, camera identification, classification of computer graphics images and detection of emerging Deepfake images. With this review it can be observed that even if image forgeries are becoming easy to create, there are several options to detect each kind of them. A review of different image databases and an overview of anti-forensic methods are also presented. Finally, we suggest some future working directions that the research community could consider to tackle in a more effective way the spread of doctored images.

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Dong, Wanli, Hui Zeng, Yong Peng, Xiaoming Gao, and Anjie Peng. "A deep learning approach with data augmentation for median filtering forensics." Multimedia Tools and Applications 81, no. 8 (February 17, 2022): 11087–105. http://dx.doi.org/10.1007/s11042-022-12040-w.

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40

Barik, Lalbihari. "Data mining approach for digital forensics task with deep learning techniques." International Journal of ADVANCED AND APPLIED SCIENCES 7, no. 5 (May 2020): 56–65. http://dx.doi.org/10.21833/ijaas.2020.05.008.

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41

Zhang, Wei, Huiling Shi, Xinming Lu, and Longquan Zhou. "A Deep Learning Solution for Multimedia Conference System Assisted by Cloud Computing." International Journal of Information Technology and Web Engineering 13, no. 3 (July 2018): 85–98. http://dx.doi.org/10.4018/ijitwe.2018070106.

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With the development of information technology, more and more people use multimedia conference system to communicate or work across regions. In this article, an ultra-reliable and low-latency solution based on Deep Learning and assisted by Cloud Computing for multimedia conference system, called UCCMCS, is designed and implemented. In UCCMCS, there are two-tiers in its data distribution structure which combines the advantages of cloud computing. And according to the requirements of ultra-reliability and low-latency, a bandwidth optimization model is proposed to improve the transmission efficiency of multimedia data so as to reduce the delay of the system. In order to improve the reliability of data distribution, the help of cloud computing node is used to carry out the retransmission of lost data. the experimental results show UCCMCS could improve the reliability and reduce the latency of the multimedia data distribution in multimedia conference system.

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42

Zheng, Yu, Jiezhong Zhu, Wei Fang, and Lian-Hua Chi. "Deep Learning Hash for Wireless Multimedia Image Content Security." Security and Communication Networks 2018 (September 25, 2018): 1–13. http://dx.doi.org/10.1155/2018/8172725.

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With the explosive growth of the wireless multimedia data on the wireless Internet, a large number of illegal images have been widely disseminated in wireless networks, which seriously endangers the content security of wireless networks. However, how to identify and classify illegal images quickly, accurately, and in real time is a key challenge for wireless multimedia networks. To avoid illegal images circulating on the Internet, each image needs to be detected, extracted features, and compared with the image in the feature library to verify the legitimacy of the image. An improved image deep learning hash (IDLH) method to learn compact binary codes for image search is proposed in this paper. Specifically, there are three major processes of IDLH: the feature extraction, deep secondary search, and image classification. IDLH performs image retrieval by the deep neural networks (DNN) as well as image classification with the binary hash codes. Different from other deep learning-hash methods that often entail heavy computations by using a conventional classifier, exemplified by K nearest neighbor (K-NN) and support vector machines (SVM), our method learns classifiers using binary hash codes, which can be learned synchronously in training. Finally, comprehensive experiments are conducted to evaluate IDLH method by using CIFAR-10 and Caltech 256 image library datasets, and the results show that the retrieval performance of IDLH method can effectively identify illegal images.

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43

Liu, Qiuli, Lu Jin, Zechao Li, and Jinhui Tang. "Multimedia retrieval by deep hashing with multilevel similarity learning." Journal of Visual Communication and Image Representation 59 (February 2019): 150–58. http://dx.doi.org/10.1016/j.jvcir.2018.11.011.

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44

Jeon, Gwanggil, Marco Anisetti, Ernesto Damiani, and Burak Kantarci. "Artificial intelligence in deep learning algorithms for multimedia analysis." Multimedia Tools and Applications 79, no. 45-46 (July 16, 2020): 34129–39. http://dx.doi.org/10.1007/s11042-020-09232-7.

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45

Zhang, Huiying, Jinjin Guo, and Guie Sun. "Multiweight Cross-Multimedia Logistics Optimal Path Exploration by Integrating High-Dimensional Deep Learning." Advances in Multimedia 2021 (December 8, 2021): 1–6. http://dx.doi.org/10.1155/2021/1474341.

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High-dimensional deep learning has been applied in all walks of life at present, among which the most representative one is the logistics path optimization combining multimedia with high-dimensional deep learning. Using multimedia logistics to explore and operate the best path can make the whole logistics industry get innovation and leap forward. How to use high-dimensional deep learning to conduct visual logistics operation management is an opportunity and a problem facing the whole logistics industry at present. The application of high-dimensional deep learning technology can help logistics enterprises improve their management level, realize intelligent decision-making, and enable accurate prediction. Starting from the total amount of logistics, regional layout, enterprise scale, and high-dimensional deep learning algorithm, this paper analyzes the current situation of China’s logistic development through multiweight analysis and explores the best path for multimedia logistics.

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46

Wang, Xinyi, He Wang, and Shaozhang Niu. "An Intelligent Forensics Approach for Detecting Patch-Based Image Inpainting." Mathematical Problems in Engineering 2020 (October 28, 2020): 1–10. http://dx.doi.org/10.1155/2020/8892989.

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Image inpainting algorithms have a wide range of applications, which can be used for object removal in digital images. With the development of semantic level image inpainting technology, this brings great challenges to blind image forensics. In this case, many conventional methods have been proposed which have disadvantages such as high time complexity and low robustness to postprocessing operations. Therefore, this paper proposes a mask regional convolutional neural network (Mask R-CNN) approach for patch-based inpainting detection. According to the current research, many deep learning methods have shown the capacity for segmentation tasks when labeled datasets are available, so we apply a deep neural network to the domain of inpainting forensics. This deep learning model can distinguish and obtain different features between the inpainted and noninpainted regions. To reduce the missed detection areas and improve detection accuracy, we also adjust the sizes of the anchor scales due to the inpainting images and replace the original nonmaximum suppression single threshold with an improved nonmaximum suppression (NMS). The experimental results demonstrate this intelligent method has better detection performance over recent approaches of image inpainting forensics.

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47

Wang, Jiayu, Hongquan Wang, Xinshan Zhu, and Pengwei Zhou. "A Deep Learning Approach in the DCT Domain to Detect the Source of HDR Images." Electronics 9, no. 12 (December 3, 2020): 2053. http://dx.doi.org/10.3390/electronics9122053.

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Although high dynamic range (HDR) is now a common format of digital images, limited work has been done for HDR source forensics. This paper presents a method based on a convolutional neural network (CNN) to detect the source of HDR images, which is built in the discrete cosine transform (DCT) domain. Specifically, the input spatial image is converted into DCT domain with discrete cosine transform. Then, an adaptive multi-scale convolutional (AMSC) layer extracts features related to HDR source forensics from different scales. The features extracted by AMSC are further processed by two convolutional layers with pooling and batch normalization operations. Finally, classification is conducted by a fully connected layer with Softmax function. Experimental results indicate that the proposed DCT-CNN outperforms the state-of-the-art schemes, especially in accuracy, robustness, and adaptability.

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48

Rhee, Kang Hyeon. "Composition of Visual Feature Vector Pattern for Deep Learning in Image Forensics." IEEE Access 8 (2020): 188970–80. http://dx.doi.org/10.1109/access.2020.3029087.

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Ferraro, Antonino, Vincenzo Moscato, and Giancarlo Sperlì. "Deep Learning-Based Community Detection Approach on Multimedia Social Networks." Applied Sciences 11, no. 23 (December 2, 2021): 11447. http://dx.doi.org/10.3390/app112311447.

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Exploiting multimedia data to analyze social networks has recently become one the most challenging issues for Social Network Analysis (SNA), leading to defining Multimedia Social Networks (MSNs). In particular, these networks consider new ways of interaction and further relationships among users to support various SNA tasks: influence analysis, expert finding, community identification, item recommendation, and so on. In this paper, we present a hypergraph-based data model to represent all the different types of relationships among users within an MSN, often mediated by multimedia data. In particular, by considering only user-to-user paths that exploit particular hyperarcs and relevant to a given application, we were able to transform the initial hypergraph into a proper adjacency matrix, where each element represents the strength of the link between two users. This matrix was then computed in a novel way through a Convolutional Neural Network (CNN), suitably modified to handle high data sparsity, in order to generate communities among users. Several experiments on standard datasets showed the effectiveness of the proposed methodology compared to other approaches in the literature.

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Lu, Yujiang, Yaju Liu, Jianwei Fei, and Zhihua Xia. "Channel-Wise Spatiotemporal Aggregation Technology for Face Video Forensics." Security and Communication Networks 2021 (August 27, 2021): 1–13. http://dx.doi.org/10.1155/2021/5524930.

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Recent progress in deep learning, in particular the generative models, makes it easier to synthesize sophisticated forged faces in videos, leading to severe threats on social media about personal privacy and reputation. It is therefore highly necessary to develop forensics approaches to distinguish those forged videos from the authentic. Existing works are absorbed in exploring frame-level cues but insufficient in leveraging affluent temporal information. Although some approaches identify forgeries from the perspective of motion inconsistency, there is so far not a promising spatiotemporal feature fusion strategy. Towards this end, we propose the Channel-Wise Spatiotemporal Aggregation (CWSA) module to fuse deep features of continuous video frames without any recurrent units. Our approach starts by cropping the face region with some background remained, which transforms the learning objective from manipulations to the difference between pristine and manipulated pixels. A deep convolutional neural network (CNN) with skip connections that are conducive to the preservation of detection-helpful low-level features is then utilized to extract frame-level features. The CWSA module finally makes the real or fake decision by aggregating deep features of the frame sequence. Evaluation against a list of large facial video manipulation benchmarks has illustrated its effectiveness. On all three datasets, FaceForensics++, Celeb-DF, and DeepFake Detection Challenge Preview, the proposed approach outperforms the state-of-the-art methods with significant advantages.

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