Interfaces (GUI), Vol. Thus will lead to the Experimental results show an improvement to the state-of-the-art on temporal splicing localization and also promising performance in the newly tackled case of spatial splicing, on both synthetic and real-world videos. All credits go towards the authors. Preprints, 2019050013. Theoretical analysis proves that the gradient vanishing in traditional MIL is relieved in S-MIL.  proposed a detection method that utilizes both the CNN and Recurrent Neural Network (RNN) to capture the temporal information presented in 5 consecutive deepfake video frames. 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. CNN part automatically builds the low-level features, and RNN part finds the relation between the features in different frames of the same event. 2018. discrepancies induced during deepfake creation around TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. properties of the algorithm and provide a regret bound on the convergence rate This work explores the analysis of spatio-temporal texture dynamics of the video signal, with the goal of characterizing and distinguishing real and fake sequences. Much like malicious computer viruses, deepfake generation , , , , , , , , , , , , , ,  and deepfake detection , . labeled data is provided for training), computational efficiency and low 618--626. The collected examples were used to devise an approximate taxonomy of criminal applications for the purpose of assessing their relative threat levels. To this end, we propose spatio-temporal features, modeled by 3D CNNs, to extend the generalization capabilities to detect new sorts of deepfake videos. the outcome DeepFake videos, in turn, these artifacts can be Our method outperforms existing methods and can accurately detect people in scenes with significant occlusions. ... Frame-based methods meet with three major difficulties when applied to fake video detection with partial faces attack. CCS CONCEPTS • Computing methodologies → Computer vision; • Security and privacy → Social aspects of security and privacy. It is best if the source environment is similar to the target. The case study part at the end also provides a cost-effective and step-by-step approach that can be replicated by others easily.
• Commercial, Legal, and Ethical Issues > Social Considerations endobj Finally, few open challenges in the field of passive video forgery detection are also described. The ACM Digital Library is published by the Association for Computing Machinery. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. Nov. 27, 2018 - The data-preserving AI learning environment model is expected to prevent cyberattacks and data deterioration that may occur when data are provided and utilized in an open network for the processing and collection of raw data. 2015. million parameters. otherwise. learning approach to detect inconsistencies in facial Among all the aspects discussed in the survey, we pay special attention to the latest generation of DeepFakes, highlighting its improvements and challenges for fake detection. Various detection techniques for Deepfake attacks have been explored. endobj In NIPS. Hsu, C. C., Zhuang, Y. X., and Lee, C. Y. For the detection part, they have tried to use a single recurrent network on top of the final features from the backbone net and also learn multiple recurrent networks at different level of the hierarchy of the backbone net. MesoNet: a Compact Facial Video Forgery Detection Network Paper This survey provides a thorough review of techniques for manipulating face images including DeepFake methods, and methods to detect such manipulations. In this work, we focus on the effect that JPEG has on CNN training considering different computer vision and forensic image classification problems. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. Long short-term memory. This method is promising but as the previous one, requires manipulation masks for training. Deepfake Video Detection Using Recurrent Neural Networks. As a result, their model shows state-of-the-art performance (an average accuracy of 0.95).
In AVSS. arXiv preprint:1802.04712 (2018). Later, a brief survey of existing passive video forgery detection techniques based on the features, forgery identified, datasets used, and performance parameters detail along with their limitations are reviewed. 3. The method exhibits invariance to diagonal detection of DeepFake videos. Different from the naive adversarial faces, our proposed approach leverages differentiable random image transformations during the generation. In particular, we consider information related to the heart rate using remote photoplethysmography (rPPG). Request the conference paper directly from the authors on ResearchGate. For deinterlaced video, we quantify the correlations introduced by the camera or software deinterlacing algorithms and show how tampering can disturb these correlations. Deepfake represents a category of face-swapping attacks that leverage machine learning models such as autoencoders or generative adversarial networks. Agarwal S, Farid H, Gu Y, et al.8 propose a high-confidence manipulation localization architecture that utilizes resampling features, long short-term memory (LSTM) cells, and an encoder–decoder network to segment out manipulated regions from non-manipulated ones. Learning long-term dependencies is possible when Exposing DeepFake Videos By Detecting Face Warping Artifacts, FaceForensics++: Learning to Detect Manipulated Facial Images, Exposing Deep Fakes Using Inconsistent Head Poses, Recurrent Convolutional Strategies for Face Manipulation Detection in Videos, ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection, Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations, Protecting World Leaders Against Deep Fakes, Detecting GAN-generated Imagery using Color Cues, Celeb-DF: A New Dataset for DeepFake Forensics, DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection, Adam: A Method for Stochastic Optimization, Rethinking the Inception Architecture for Computer Vision, Long-term recurrent convolutional networks for visual recognition and description, Image-to-Image Translation with Conditional Adversarial Networks, Learning realistic human actions from movies, TensorFlow: A system for large-scale machine learning, Recurrent Convolutional Network for Video-Based Person Re-identification, Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks, 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). classifiers by achieving an accuracy of 99.65% and AUROC of Detecting these videos is a massive problem because of nonlinearities are incorporated into the network state updates. report 3.5% top-5 error and 17.3% top-1 error. For manipulation detection, we use a recurrent-convolutional network similar to [10, 13], where the input is a sequence of frames from the query video. The LSTM RNN allowed for the network to keep track of temporal differences between a deepfake and a benign video. They found landmark-based face alignment with bidirectional-recurrent-densenet have good performance. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom designed ASICs known as Tensor Processing Units (TPUs).
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