Masi deepfake

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Though a common assumption is that adversarial points leave the manifold of the input data, our study finds out that, surprisingly, untargeted adversarial points in the input space are very likely under the generative model hidden inside the discriminative classifier -- have low energy in the EBM. As a result, the algorithm is encouraged to learn both comprehensive features and inherent hierarchical nature of different forgery attributes, thereby improving the IFDL representation. Jay Kuo , Iacopo Masi. We offer a method for one-shot mask-guided image synthesis that allows controlling manipulations of a single image by inverting a quasi-robust classifier equipped with strong regularizers. Image Generation. Adversarial Attack Adversarial Robustness.

Masi deepfake

Title: Towards a fully automatic solution for face occlusion detection and completion. Abstract: Computer vision is arguably the most rapidly evolving topic in computer science, undergoing drastic and exciting changes. A primary goal is teaching machines how to understand and model humans from visual information. The main thread of my research is giving machines the capability to 1 build an internal representation of humans, as seen from a camera in uncooperative environments, that is highly discriminative for identity e. In this talk, I show how to enforce smoothness in a deep neural network for better, structured face occlusion detection and how this occlusion detection can ease the learning of the face completion task. Finally, I quickly introduce my recent work on Deepfake Detection. Bio: Dr. Masi earned his Ph. Immediately after, he moved to California and joined USC, where he was a postdoctoral scholar. Skip to main content. Home In the news Towards a fully automatic solution for face occlusion detection and completion.

Nguyen T.

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The current spike of hyper-realistic faces artificially generated using deepfakes calls for media forensics solutions that are tailored to video streams and work reliably with a low false alarm rate at the video level. We present a method for deepfake detection based on a two-branch network structure that isolates digitally manipulated faces by learning to amplify artifacts while suppressing the high-level face content. Unlike current methods that extract spatial frequencies as a preprocessing step, we propose a two-branch structure: one branch propagates the original information, while the other branch suppresses the face content yet amplifies multi-band frequencies using a Laplacian of Gaussian LoG as a bottleneck layer. To better isolate manipulated faces, we derive a novel cost function that, unlike regular classification, compresses the variability of natural faces and pushes away the unrealistic facial samples in the feature space. We then offer a full, detailed ablation study of our network architecture and cost function. Finally, although the bar is still high to get very remarkable figures at a very low false alarm rate, our study shows that we can achieve good video-level performance when cross-testing in terms of video-level AUC. Iacopo Masi. Aditya Killekar. Royston Marian Mascarenhas. Shenoy Pratik Gurudatt.

Masi deepfake

Federal government websites often end in. The site is secure. The following information was supplied regarding data availability:. Celeb-df: A large-scale challenging dataset for deepfake forensics. The Python scripts are available in the Supplemental Files. Recently, the deepfake techniques for swapping faces have been spreading, allowing easy creation of hyper-realistic fake videos.

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Figure 5. Kumar A. Blazeface: Sub-millisecond neural face detection on mobile gpus. Chen T. In Dang et al. Then, four deep learning models, ResNet, ResNet50, VGG16, and ResNet, are applied to discover the artifacts from face frames based on the inconsistencies in resolution between the warped face region and its surrounding context. Dang H. Zhang K. Exposing deepfake videos by detecting face warping artifacts. A face preprocessing approach for improved deepfake detection. PeerJ Comput. Additionally, a fully connected layer is added as an output layer. In Dave et al.

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Title: Towards a fully automatic solution for face occlusion detection and completion. A face preprocessing approach for improved deepfake detection. Mehra A. The formula of XGBoost model is given by. Pattern Recognit. None, These blocks are followed by Bi-LSTM to learn the temporal information and detect the deepfake videos. Terms Data policy Cookies policy from. The main thread of my research is giving machines the capability to 1 build an internal representation of humans, as seen from a camera in uncooperative environments, that is highly discriminative for identity e. To train the proposed model, the authentic Celeb-DF videos and fake videos selected randomly from the Celeb-DF fake videos are employed. De Lima O. As a result, analyzing and detecting faces from photos or videos constitute a central role in detecting fakes. Dozat T.