MMD Based Discriminative Learning for Face Forgery Detection

Han, Jian; Gevers, Theo

doi:10.1007/978-3-030-69541-5_8

Cited by 8 publications

(6 citation statements)

References 35 publications

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“…Such features were used as input to the these networks for detecting Deepfake manipulations. Besides data augmentation [57], super-resolution reconstruction [58], localization strategies in pixel levels [11] are formulated on the entire frame, and maximum mean discrepancy (MMD) loss [59] is applied to discover a more general feature.…”

Section: ) Deep Learning Based Methodsmentioning

confidence: 99%

“…• The information is stored in a permission-based Blockchain, which gives the owner control over its contents. Based on the studies, taking together all these methods, Table 3 [11], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [86],…”

Section: • Integrates the Critical Features Of Ipfs [114]-basedmentioning

confidence: 99%

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Deepfake Detection: A Systematic Literature Review

et al. 2022

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Over the last few decades, rapid progress in AI, machine learning, and deep learning has resulted in new techniques and various tools for manipulating multimedia. Though the technology has been mostly used in legitimate applications such as for entertainment and education, etc., malicious users have also exploited them for unlawful or nefarious purposes. For example, high-quality and realistic fake videos, images, or audios have been created to spread misinformation and propaganda, foment political discord and hate, or even harass and blackmail people. The manipulated, high-quality and realistic videos have become known recently as Deepfake. Various approaches have since been described in the literature to deal with the problems raised by Deepfake. To provide an updated overview of the research works in Deepfake detection, we conduct a systematic literature review (SLR) in this paper, summarizing 112 relevant articles from 2018 to 2020 that presented a variety of methodologies. We analyze them by grouping them into four different categories: deep learning-based techniques, classical machine learning-based methods, statistical techniques, and blockchain-based techniques. We also evaluate the performance of the detection capability of the various methods with respect to different datasets and conclude that the deep learning-based methods outperform other methods in Deepfake detection.

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Section: ) Deep Learning Based Methodsmentioning

confidence: 99%

Section: • Integrates the Critical Features Of Ipfs [114]-basedmentioning

confidence: 99%

Deepfake Detection: A Systematic Literature Review

et al. 2022

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“…They also evaluated a set of transfer learning models adapted to forgery detection, namely, ResNet, VGG-19, and DenseNet. Han et al [49] applied Maximum Mean Discrepancy (MMD) loss to train a machine learning model for forgery detection. The authors evaluated their proposed approach on the DF-TIMIT, UADFV, Celeb-DF, and FaceForensics++ datasets.…”

Section: Seam Carving and Relevant Image Forgery Detectionmentioning

confidence: 99%

A comparison study to detect seam carving forgery in JPEG images with deep learning models

Celebi¹,

Hsu²

2022

J Surveill Secur Saf

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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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“…In [9,10], the authors used bi-directional LSTM and the features in both the frequency and spatial domain to boost the detection accuracy. In [11], Jian et al designed a maximum mean discrepancy (MMD) loss, along with triplet loss and center loss to separate the distributions of authentic and fake media. In [12], Li et al aimed to predict the blending boundaries in forged face images, which only focused on the post-processing step and can detect various forgery methods.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the data-driven Deepfake detection approaches can achieve good detection results in the dataset on which they are trained for. However, their performance usually declines when cross-dataset tests are conducted [11]. To solve the above problem, a new Deepfake detection method based on the deep neural network is proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Improving the Generalization Ability of Deepfake Detection via Disentangled Representation Learning

Hu¹,

Wang²,

Li³

2021

2021 IEEE International Conference on Image Processing (ICIP)

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Deepfake refers to a deep learning based technology which can synthesize visually realistic face images/videos. The misuse of this technology poses a great threat to the society. Although numerous approaches have been proposed to detect Deepfake forgeries, their generalization ability on unseen datasets is limited. In this paper, we propose a new approach that detects human face forgeries by automatically locating the forgery-related region to make the final decision. The proposed network contains two modules, including: the disentanglement module to extract forgery relevant information and the classification module to detect the manipulation artifacts from various regions at different scales. The experiment results on three widely used Deepfake datasets show that the proposed approach can achieve high detection accuracies and outperforms several state-of-the-arts methods especially when evaluated on the unseen datasets.

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MMD Based Discriminative Learning for Face Forgery Detection

Cited by 8 publications

References 35 publications

Deepfake Detection: A Systematic Literature Review

Deepfake Detection: A Systematic Literature Review

A comparison study to detect seam carving forgery in JPEG images with deep learning models

Improving the Generalization Ability of Deepfake Detection via Disentangled Representation Learning

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