Face Manipulation Detection Based on Supervised Multi-Feature Fusion Attention Network

Nowadays, faces in videos can be easily replaced with the development of deep learning, and these manipulated videos are realistic and cannot be distinguished by human eyes. Some people maliciously use the technology to attack others, especially celebrities and politicians, causing destructive social impacts. Therefore, it is imperative to design an accurate method for detecting face manipulation. However, most of the existing methods adopt single convolutional neural network as the feature extraction module, causing the extracted features to be inconsistent with the human visual mechanism. Moreover, the rich details and semantic information cannot be reflected with single feature, limiting the detection performance. Therefore, this paper tackles the above problems by proposing a novel face manipulation detection method based on a supervised multi-feature fusion attention network (SMFAN). Specifically, the capsule network is used for face manipulation detection, and the SMFAN is added to the original capsule network to extract details of the fake face image. Further, the focal loss is used to realize hard example mining. Finally, the experimental results on the public dataset FaceForensics++ show that the proposed method has better performance.

A Novel Defensive Strategy for Facial Manipulation Detection Combining Bilateral Filtering and Joint Adversarial Training

Facial manipulation enables facial expressions to be tampered with or facial identities to be replaced in videos. The fake videos are so realistic that they are even difficult for human eyes to distinguish. This poses a great threat to social and public information security. A number of facial manipulation detectors have been proposed to address this threat. However, previous studies have shown that the accuracy of these detectors is sensitive to adversarial examples. The existing defense methods are very limited in terms of applicable scenes and defense effects. This paper proposes a new defense strategy for facial manipulation detectors, which combines a passive defense method, bilateral filtering, and a proactive defense method, joint adversarial training, to mitigate the vulnerability of facial manipulation detectors against adversarial examples. The bilateral filtering method is applied in the preprocessing stage of the model without any modification to denoise the input adversarial examples. The joint adversarial training starts from the training stage of the model, which mixes various adversarial examples and original examples to train the model. The introduction of joint adversarial training can train a model that defends against multiple adversarial attacks. The experimental results show that the proposed defense strategy positively helps facial manipulation detectors counter adversarial examples.

Forecasting economic result of manipulation using game theory modeling

Manipulations are taking place widely on various capital, commodity, derivative and other markets. They are reported regularly and sometimes causing significant losses. But it doesn’t mean that the efforts intended to limit this sort of activity are insignificant. Surveillance budgets, as well as applied fines, are impressing. The annual volume of manipulative attempts and the efforts, intended to deter these attempts, are growing exponentially year after year. The imperfection and low versatility of detection methods are leaving space for successful attempts, making manipulative behavior still attractive. This paper is representing the model, based on the Game Theory and aimed to fit modern requirements of surveillance. The article defines basic problems in manipulation detection and proves model’s capability to solve them. However, the problem is reviewed on a general level allowing to elaborate the versatile model, but not a specific manipulative scenario. At the same time, the model allows complementing it with precise tools defining aspects related to actual manipulation. Manipulation and the shaping of it's economic results are reviewed in-depth, revealing it's core phenomenology.