Generalizable Deepfake Detection Based on Forgery-aware Layer Masking and Multi-artifact Subspace Decomposition
Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology（南京信息工程大学数字取证教育部工程研究中心）
Deepfake detection remains highly challenging, particularly in cross-dataset scenarios and complex real-world settings. This challenge mainly arises because artifact patterns vary substantially across different forgery methods, whereas adapting pretrained models to such artifacts often overemphasizes forgery-specific cues and disturbs semantic representations, thereby weakening generalization. Existing approaches typically rely on full-parameter fine-tuning or auxiliary supervision to improve discrimination. However, they often struggle to model diverse forgery artifacts without compromising pretrained representations. To address these limitations, we propose FMSD, a deepfake detection framework built upon Forgery-aware Layer Masking and Multi-Artifact Subspace Decomposition. Specifically, Forgery-aware Layer Masking evaluates the bias-variance characteristics of layer-wise gradients to identify forgery-sensitive layers, thereby selectively updating them while reducing unnecessary disturbance to pretrained representations. Building upon this, Multi-Artifact Subspace Decomposition further decomposes the selected layer weights via Singular Value Decomposition (SVD) into a semantic subspace and multiple learnable artifact subspaces. These subspaces are optimized to capture heterogeneous and complementary forgery artifacts, enabling effective modeling of diverse forgery patterns while preserving pretrained semantic representations. Furthermore, orthogonality and spectral consistency constraints are imposed to regularize the artifact subspaces, reducing redundancy across them while preserving the overall spectral structure of pretrained weights.
Towards Generalizable Deepfake Detection via Forgery-aware Audio-Visual Adaptation: A Variational Bayesian Approach
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Department of New Networks, Pengcheng Laboratory（鹏城实验室网络部）；School of Cyber Science and Technology, Sun Yat-sen University（中山大学网络安全科学与技术学院）；School of Computer Science and Cyber Engineering, Guangzhou University（广州大学计算机科学与网络工程学院）；School of Cyberspace Science, Harbin Institute of Technology（哈尔滨工业大学网络空间科学学院）
Forgery Guided Learning Strategy with Dual Perception Network for Deepfake Cross-domain Detection
School of Computer Science and Technology, Xinjiang University, Urumqi, China（新疆大学计算机科学与技术学院）；Xinjiang Multimodal Intelligent Processing and Information Security Engineering Technology Research Center, Urumqi, China（新疆多模态智能处理与信息安全工程技术研究中心）；College of Computer Science and Electronic Engineering, Hunan University, Changsha, China（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, New Paltz, New York, USA（纽约州立大学新帕尔兹分校计算机科学系）
Universitas Mercatorum（梅尔卡托鲁姆大学）；National Inter-University Consortium for Telecommunications (CNIT)（全国校际电信联盟（CNIT））
Deepfake Generation Techniques are evolving at a rapid pace, making it possible to create realistic manipulated images and videos and endangering the serenity of modern society. The continual emergence of new and varied techniques brings with it a further problem to be faced, namely the ability of deepfake detection models to update themselves promptly in order to be able to identify manipulations carried out using even the most recent methods. This is an extremely complex problem to solve, as training a model requires large amounts of data, which are difficult to obtain if the deepfake genera…
DeepFake-Adapter: Dual-Level Adapter for DeepFake Detection
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）；S-Lab, Nanyang Technological University（南洋理工大学S-Lab）
Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture
Rapid-Rich Object Search (ROSE) Lab, School of Electrical and Electronic Engineering, Nanyang Technology University（快速丰富对象搜索实验室（ROSE实验室），电子与电气工程学院，南洋理工大学）；School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics（计算机与人工智能学院，江西财经大学）；Department of Computer Science, Hong Kong Baptist University（计算机科学系，香港 Baptist大学）；Department of Electrical Engineering, City University of Hong Kong（电气工程系，香港城市大学）；Department of Computer Science and Computing, Zhejiang University City College（计算机科学与计算系，浙江大学城市学院）；Artificial Intelligence Lab. ( Recod.ai ) at the University of Campinas（Campinas大学的人工智能实验室（Recod.ai））
University of Technology Sydney（悉尼科技大学）；Swinburne University of Technology（斯威本科技大学）；City University of Macau（澳门城市大学）
DeepFakes are raising significant social concerns. Although various DeepFake detectors have been developed as forensic countermeasures, these detectors are still vulnerable to attacks. Recently, a few attacks, principally adversarial attacks, have succeeded in cloaking DeepFake images to evade detection. However, these attacks have typical detector-specific designs, which require prior knowledge about the detector, leading to poor transferability. Moreover, these attacks only consider simple security scenarios. Less is known about how effective they are in high-level scenarios where either the…
Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection
The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen)（香港中文大学（深圳））；University at Buffalo, State University of New York（纽约州立大学布法罗分校）；Nanjing University of Information Science and Technology（南京信息工程大学）
DeepShield: Fortifying Deepfake Video Detection with Local and Global Forgery Analysis
Sun Yat-sen University（中山大学）；Pengcheng Laboratory（鹏城实验室）；Guilin University of Electronic Technology（桂林电子科技大学）；Guangdong Key Laboratory of Big Data Analysis and Processing（广东大数据分析与处理重点实验室）
DDL: A Large-Scale Dataset for Deepfake Detection and Localization in Diversified Real-World Scenarios
AntGroup（蚂蚁集团）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Hefei University of Technology（合肥工业大学）；Anhui Province Key Laboratory of Digital Security（安徽省数字安全重点实验室）；A ⋆ STAR Centre for Frontier AI Research（A ⋆ STAR前沿人工智能研究中心）
DeepfakeBench-MM: A Comprehensive Benchmark for Multimodal Deepfake Detection
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University at Buffalo, State University of New York（纽约州立大学布法罗分校）；Nanjing University of Posts and Telecommunications（南京邮电大学）
Towards Real-World Deepfake Detection: A Diverse In-the-wild Dataset of Forgery Faces
School of Cyber Science and Engineering, Huazhong University of Science and Technology（华中科技大学信息科学与工程学院）；School of Software Engineering, Huazhong University of Science and Technology（华中科技大学软件工程学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）；School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）
Can We Leave Deepfake Data Behind in Training Deepfake Detector?
School of Computer Science, Wuhan University（武汉大学计算机学院）；The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen)（香港中文大学（深圳））；WeChat, Tencent（腾讯微信）
As ultra-realistic face forgery techniques emerge, deepfake detection has attracted increasing attention due to security concerns. Many detectors cannot achieve accurate results when detecting unseen manipulations despite excellent performance on known forgeries. In this paper, we are motivated by the observation that the discrepancies between real and fake videos are extremely subtle and localized, and inconsistencies or irregularities can exist in some critical facial regions across various information domains. To this end, we propose a novel pipeline, Cross-Domain Local Forensics (XDLF), fo…
The University of Adelaide（阿德莱德大学）；Tencent AI Lab（腾讯人工智能实验室）
Recent studies in deepfake detection have yielded promising results when the training and testing face forgeries are from the same dataset. However, the problem remains challenging when one tries to generalize the detector to forgeries created by unseen methods in the training dataset. This work addresses the generalizable deepfake detection from a simple principle: a generalizable representation should be sensitive to diverse types of forgeries. Following this principle, we propose to enrich the "diversity" of forgeries by synthesizing augmented forgeries with a pool of forgery configurations…
Towards Sustainable Universal Deepfake Detection with Frequency-Domain Masking
Singapore University of Technology and Design (SUTD)（新加坡科技设计大学）；Faculty of Science and Technology (REALTEK), Norwegian University of Life Sciences (NMBU)（科学与技术学院（REALTEK）、挪威生命科学大学）
FairForensics: Seeing Expressions and Parsing Demographics via Vision-Language Modeling for Generalizable Fair Deepfake Detection
Shenzhen University（深圳大学）；Shandong Artificial Intelligence Institute（山东省人工智能研究院）；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；Tianjin University of Technology（天津理工大学）
Environment-Invariant Subspace Learning for Generalizable Deepfake Detection
Tianjin University of Technology（天津理工大学）；Shandong Artificial Intelligence Institute（山东人工智能研究院）；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））
Locate and Verify: A Two-Stream Network for Improved Deepfake Detection
Zhejiang University（浙江大学）；ZJU-Hangzhou Global Scientific and Technological Innovation Center（浙江大学杭州国际科创中心）；Black Sesame Technologies（黑芝麻智能科技）；University College London（伦敦大学学院）
Shandong University（山东大学）；National University of Singapore（新加坡国立大学）；The University of Hong Kong（香港大学）；University of Technology Sydney（悉尼科技大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
Detecting forgery videos is highly desirable due to the abuse of deepfake. Existing detection approaches contribute to exploring the specific artifacts in deepfake videos and fit well on certain data. However, the growing technique on these artifacts keeps challenging the robustness of traditional deepfake detectors. As a result, the development of generalizability of these approaches has reached a blockage. To address this issue, given the empirical results that the identities behind voices and faces are often mismatched in deepfake videos, and the voices and faces have homogeneity to some ex…
Recent advances in face forgery techniques produce nearly visually untraceable deepfake videos, which could be leveraged with malicious intentions. As a result, researchers have been devoted to deepfake detection. Previous studies have identified the importance of local low-level cues and temporal information in pursuit to generalize well across deepfake methods, however, they still suffer from robustness problem against post-processings. In this work, we propose the Local- & Temporal-aware Transformer-based Deepfake Detection (LTTD) framework, which adopts a local-to-global learning protocol …
Deepfake Forensics Adapter: A Dual-Stream Network for Generalizable Deepfake Detection
Shenzhen Technology University（深圳技术大学）；Singapore Institute of Technology（新加坡理工学院）；The University of Hong Kong（香港大学）；Nanyang Technological University（南洋理工大学）
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）
With advancements of deep learning techniques, it is now possible to generate super-realistic images and videos, i.e., deepfakes. These deepfakes could reach mass audience and result in adverse impacts on our society. Although lots of efforts have been devoted to detect deepfakes, their performance drops significantly on previously unseen but related manipulations and the detection generalization capability remains a problem. Motivated by the fine-grained nature and spatial locality characteristics of deepfakes, we propose Locality-Aware AutoEncoder (LAE) to bridge the generalization gap. In t…
Indian Institute of Technology Ropar（印度理工学院罗帕尔分校）；Monash University（莫纳什大学）
We propose detection of deepfake videos based on the dissimilarity between the audio and visual modalities, termed as the Modality Dissonance Score (MDS). We hypothesize that manipulation of either modality will lead to dis-harmony between the two modalities, eg, loss of lip-sync, unnatural facial and lip movements, etc. MDS is computed as an aggregate of dissimilarity scores between audio and visual segments in a video. Discriminative features are learnt for the audio and visual channels in a chunk-wise manner, employing the cross-entropy loss for individual modalities, and a contrastive loss…
University of Science and Technology of China（中国科学技术大学）；Microsoft Cloud AI（微软云人工智能）
Face forgery by deepfake is widely spread over the internet and has raised severe societal concerns. Recently, how to detect such forgery contents has become a hot research topic and many deepfake detection methods have been proposed. Most of them model deepfake detection as a vanilla binary classification problem, i.e, first use a backbone network to extract a global feature and then feed it into a binary classifier (real/fake). But since the difference between the real and fake images in this task is often subtle and local, we argue this vanilla solution is not optimal. In this paper, we ins…
Learning Real Facial Concepts for Independent Deepfake Detection
School of Software, Shandong University（山东大学软件学院）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；Nanyang Technological University（南洋理工大学）
Weakly Supervised Multimodal Temporal Forgery Localization via Multitask Learning
School of Computer Science and Engineering, MoE Key Laboratory of Information Technology, Guangdong Province Key Laboratory of Information Security Technology, Sun Yat-sen University（计算机科学与工程学院、信息科技教育部重点实验室、广东省信息安全技术重点实验室、中山大学）；State Key Laboratory of Mathematical Engineering and Advanced Computing（数学工程与先进计算国家重点实验室）
