Deepfakes are synthetically generated images, videos or audios, which fraudsters use to manipulate legitimate information. Current deepfake detection systems struggle against unseen data. To address this, we employ three different deep Convolutional Neural Network (CNN) models, (1) VGG16, (2) InceptionV3, and (3) XceptionNet to classify fake and real images extracted from videos. We also constructed a fusion of the deep CNN models to improve the robustness and generalisation capability. The proposed technique outperforms state-of-the-art models with 96.5% accuracy, when tested on publicly available DeepFake Detection Challenge (DFDC) test data, comprising of 400 videos. The fusion model achieves 99% accuracy on lower quality DeepFake-TIMIT dataset videos and 91.88% on higher quality DeepFake-TIMIT videos. In addition to this, we prove that prediction fusion is more robust against adversarial attacks. If one model is compromised by an adversarial attack, the prediction fusion does not let it affect the overall classification.
Adversarially Robust Deepfake Detection via Adversarial Feature Similarity Learning
Research Center for Information Technology Innovation, Academia Sinica（中央研究院資訊科技創新研究中心）；National Chengchi University（國立政治大學）；National Taiwan University（國立臺灣大學）
Recent advances in video manipulation techniques have made the generation of fake videos more accessible than ever before. Manipulated videos can fuel disinformation and reduce trust in media. Therefore detection of fake videos has garnered immense interest in academia and industry. Recently developed Deepfake detection methods rely on deep neural networks (DNNs) to distinguish AI-generated fake videos from real videos. In this work, we demonstrate that it is possible to bypass such detectors by adversarially modifying fake videos synthesized using existing Deepfake generation methods. We furt…
ByteDance AI Lab（字节跳动人工智能实验室）；Harbin Institute of Technology（哈尔滨工业大学）
With the progress in AI-based facial forgery (i.e., deepfake), people are increasingly concerned about its abuse. Albeit effort has been made for training classification (also known as deepfake detection) models to recognize such forgeries, existing models suffer from poor generalization to unseen forgery technologies and high sensitivity to changes in image/video quality. In this paper, we advocate adversarial training for improving the generalization ability to both unseen facial forgeries and unseen image/video qualities. We believe training with samples that are adversarially crafted to at…
The internet is filled with fake face images and videos synthesized by deep generative models. These realistic DeepFakes pose a challenge to determine the authenticity of multimedia content. As countermeasures, artifact-based detection methods suffer from insufficiently fine-grained features that lead to limited detection performance. DNN-based detection methods are not efficient enough, given that a DeepFake can be created easily by mobile apps and DNN-based models require high computational resources. For the first time, we show that DeepFake faces have fewer feature points than real ones, e…
ED$^4$: Explicit Data-level Debiasing for Deepfake Detection
School of computer science, Wuhan University（武汉大学计算机学院）；WeChat, Tencent Inc.（腾讯公司）；School of electronic and computer engineering, Peking University Shenzhen Graduate School（北京大学深圳研究生院电子与计算机工程学院）
DeepAgent: A Dual Stream Multi Agent Fusion for Robust Multimodal Deepfake Detection
Applied Artificial Intelligence and Intelligent Systems (AAIINS) Laboratory（应用人工智能与智能系统实验室）；Department of Computer Science and Engineering（计算机科学与工程系）；University of Scholars（学者大学）；Faculty of Science and Information Technology（科学与信息技术学院）；Faculty of Science and Technology（科学与技术学院）
SPARK-IL: Spectral Retrieval-Augmented RAG for Knowledge-driven Deepfake Detection via Incremental Learning
Univ. Polytechnique Hauts-de-France（上法兰西理工大学）；Institute of Applied Sciences and Intelligent Systems – Lecce（应用科学与智能系统研究所（莱切））；Department of Innovation Engineering, University of Salento（萨伦托大学创新工程系）；Sorbonne Center for Artificial Intelligence, Sorbonne University Abu Dhabi（索邦大学阿布扎比分校索邦人工智能中心）
Evidence Packing for Cross-Domain Image Deepfake Detection with LVLMs
Anhui University（安徽大学）；Hefei University of Technology（合肥工业大学）；IAI, Hefei Comprehensive National Science Center（合肥综合国家科学中心人工智能研究院）；United Arab Emirates University（阿拉伯联合酋长国大学）
University of Macau（澳门大学）；Macau University of Science and Technology（澳门科技大学）
With the continuous research on Deepfake forensics, recent studies have attempted to provide the fine-grained localization of forgeries, in addition to the coarse classification at the video-level. However, the detection and localization performance of existing Deepfake forensic methods still have plenty of room for further improvement. In this work, we propose a Spatial-Temporal Deepfake Detection and Localization (ST-DDL) network that simultaneously explores spatial and temporal features for detecting and localizing forged regions. Specifically, we design a new Anchor-Mesh Motion (AMM) algor…
Towards Robust DeepFake Detection under Unstable Face Sequences: Adaptive Sparse Graph Embedding with Order-Free Representation and Explicit Laplacian Spectral Prior
Institute of Intelligent Systems, College of Artificial Intelligence and Green Energy, National Yang Ming Chiao Tung University（智能系统研究所，人工智能与绿色能源学院，国立阳明交通大学）；Institute of Data Science, National Cheng Kung University（数据科学研究所，国立成功大学）
Deepfake represents a category of face-swapping attacks that leverage machine learning models such as autoencoders or generative adversarial networks. Although the concept of the face-swapping is not new, its recent technical advances make fake content (e.g., images, videos) more realistic and imperceptible to Humans. Various detection techniques for Deepfake attacks have been explored. These methods, however, are passive measures against Deepfakes as they are mitigation strategies after the high-quality fake content is generated. More importantly, we would like to think ahead of the attackers…
Counterfactual Explanations for Face Forgery Detection via Adversarial Removal of Artifacts
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Nanjing University of Information Science and Technology（南京信息工程大学）
DiffMark: Diffusion-based Robust Watermark Against Deepfakes
College of Computer Science and Technology, Xinjiang University（新疆大学计算机科学与技术学院）；Silk Road Multilingual Cognitive Computing International Cooperation Joint Laboratory（丝绸之路多语种认知计算国际合作联合实验室）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机科学与信息工程学院）；College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；Department of Computer Science, State University of New York（纽约州立大学计算机科学系）
SGF-CDNet: A Consistency-Discrepancy Graph Network over Semantic-Geometric Fused Nodes for Face Forgery Detection
School of Cyber Science and Technology, University of Science and Technology of China（中国科学技术大学网络空间安全学院）；Anhui Province Key Laboratory of Digital Security（安徽省数字安全重点实验室）
With the rapid development of deep learning technology, more and more face forgeries by deepfake are widely spread on social media, causing serious social concern. Face forgery detection has become a research hotspot in recent years, and many related methods have been proposed until now. For those images with low quality and/or diverse sources, however, the detection performances of existing methods are still far from satisfactory. In this paper, we propose an improved Xception with dual attention mechanism and feature fusion for face forgery detection. Different from the middle flow in origin…
Medical endoscopy remains a challenging application for simultaneous localization and mapping (SLAM) due to the sparsity of image features and size constraints that prevent direct depth-sensing. We present a SLAM approach that incorporates depth predictions made by an adversarially-trained convolutional neural network (CNN) applied to monocular endoscopy images. The depth network is trained with synthetic images of a simple colon model, and then fine-tuned with domain-randomized, photorealistic images rendered from computed tomography measurements of human colons. Each image is paired with an …
