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Unknown Presentation Attack Detection against Rational Attackers
TL;DR: A new optimization criterion is proposed and a set of requirements are defined for improving the performance of these systems in real-life settings and a novel detection technique is proposed using generator-based feature sets that are not biased towards any specific attack species.
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Abstract: Despite the impressive progress in the field of presentation attack detection and multimedia forensics over the last decade, these systems are still vulnerable to attacks in real-life settings. Some of the challenges for existing solutions are the detection of unknown attacks, the ability to perform in adversarial settings, few-shot learning, and explainability. In this study, these limitations are approached by reliance on a game-theoretic view for modeling the interactions between the attacker and the detector. Consequently, a new optimization criterion is proposed and a set of requirements are defined for improving the performance of these systems in real-life settings. Furthermore, a novel detection technique is proposed using generator-based feature sets that are not biased towards any specific attack species. To further optimize the performance on known attacks, a new loss function coined categorical margin maximization loss (C-marmax) is proposed which gradually improves the performance against the most powerful attack. The proposed approach provides a more balanced performance across known and unknown attacks and achieves state-of-the-art performance in known and unknown attack detection cases against rational attackers. Lastly, the few-shot learning potential of the proposed approach is studied as well as its ability to provide pixel-level explainability.
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Citations
Deepfakes Generation and Detection: A Short Survey
TL;DR: In this paper , the authors present an overview of the techniques and works that have been carried out in recent years for deepfake and face manipulations, including identity swap, face reenactment, attribute manipulation, and entire face synthesis.
Towards Face Presentation Attack Detection Based on Residual Color Texture Representation
TL;DR: Wang et al. as discussed by the authors designed a face presentation attack detector based on residual color texture representation (RCTR), which adopts DW-filter for obtaining residual image, which can effectively improve the detection efficiency.
Video and Audio Deepfake Datasets and Open Issues in Deepfake Technology: Being Ahead of the Curve
Zahid Akhtar,Thanvi Lahari Pendyala,Virinchi Sai Athmakuri +2 more
TL;DR: This paper comprehensively reviews existing image, video, and audio deepfake databases with the aim of propelling next-generation deepfake detectors for enhanced accuracy, generalization, robustness, and explainability.
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Mathematical modeling analysis of potential attack detection in topology network based on convolutional neural network
TL;DR: In this paper , a mathematical modeling analysis method for potential attack detection based on convolutional neural networks is proposed, which determines the potential attack risk assessment function through the feature extraction of vulnerable areas in network topology and the probability model of potential attacks, and then detects potential attacks by means of CNN data modeling.
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