Combined spatial and frequency dual stream network for face forgery detection [PDF]
PeerJ Computer ScienceWith the development of generative model, the cost of facial manipulation and forgery is becoming lower and lower. Fraudulent data has brought numerous hidden threats in politics, privacy, and cybersecurity.
Hui Zhao +3 more
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Learning Local Texture and Global Frequency Clues for Face Forgery Detection [PDF]
BiomimeticsIn recent years, the rapid advancement of deep learning techniques has significantly propelled the development of face forgery methods, drawing considerable attention to face forgery detection.
Xin Jin +6 more
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FreqMamba: Spatial–Frequency Fusion and State Space Sequence Modeling for Deepfake Detection [PDF]
SensorsThe rapid evolution of deepfake generation techniques has made high-fidelity facial manipulation a critical threat to social credibility and personal privacy, demanding detection algorithms with strong cross-domain generalization. Existing methods suffer
Zhiqi Li +4 more
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Exposing Face Manipulation Based on Generative Adversarial Network–Transformer and Fake Frequency Noise Traces [PDF]
SensorsIn recent years, with the application of GANs and diffusion generative network algorithms, many highly realistic synthetic images are emerging, greatly increasing the potential for misuse, and deepfakes have become a serious social concern.
Qiaoyue Man, Young-Im Cho
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Multiple contexts and frequencies aggregation network for deepfake detection. [PDF]
PLoS ONEDeepfake detection faces increasing challenges since the fast growth of generative models in developing massive and diverse Deepfake technologies. Recent advances rely on introducing heuristic features from spatial or frequency domains rather than ...
Zifeng Li +4 more
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TSFF-Net: A deep fake video detection model based on two-stream feature domain fusion. [PDF]
PLoS ONEWith the advancement of deep forgery techniques, particularly propelled by generative adversarial networks (GANs), identifying deepfake faces has become increasingly challenging.
Hangchuan Zhang +4 more
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Generalizing Face Forgery Detection with High-frequency Features [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 Current face forgery detection methods achieve high accuracy under the within-database scenario where training and testing forgeries are synthesized by the same algorithm. However, few of them gain satisfying performance under the cross-database scenario where training and testing forgeries are synthesized by different algorithms.
Yuchen Luo +3 more
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Local Relation Learning for Face Forgery Detection
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 With the rapid development of facial manipulation techniques, face forgery has received considerable attention in digital media forensics due to security concerns. Most existing methods formulate face forgery detection as a classification problem and utilize binary labels or manipulated region masks as supervision.
Shen Chen 0004 +5 more
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Forgery Face Detection Based on Multi-scale Transformer Fusing Multi-domain Information [PDF]
Jisuanji kexue, 2023 At present,the proliferation of “face-changing” fake videos generated based on deep forgery technologies such as Deepfakes poses a considerable threat to citizens' privacy and national political security.Therefore,it is of great significance to study ...
MA Xin, JI Lixin, LI Shaomei
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Face Forgery Detection and Attribution via Prototype Disentanglement [PDF]
Jisuanji kexue yu tansuoThe detection and attribution of face forgery aims to determine whether a face in an image or video has been manipulated or synthesized using Deepfake techniques, as well as to further analyze the Deepfake method behind it.
QIAN Fei, LI Wei, CHEN Peng, CHEN Haoran, XIE Lipeng, LIU Liyuan
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