Nowadays, forgery faces pose pressing security concerns over fake news, fraud, impersonation, etc. Despite the demonstrated success in intra-domain face forgery detection, existing detection methods lack generalization capability and tend to suffer from dramatic performance drops when deployed to unforeseen domains. To mitigate this issue, this paper designs a more general fake face detection model based on the vision transformer(ViT) architecture. In the training phase, the pretrained ViT weights are freezed, and only the Low-Rank Adaptation(LoRA) modules are updated. Additionally, the Single Center Loss(SCL) is applied to supervise the training process, further improving the generalization capability of the model. The proposed method achieves state-of-the-arts detection performances in both cross-manipulation and cross-dataset evaluations.
In the current era, image manipulation is becoming increasingly easier, yielding more natural looking images, owing to the modern tools in image processing and computer vision techniques. The task of the segregation of forged images has become very challenging. To tackle such problems, publicly available datasets are insufficient. In this paper, we propose to create a synthetic forged dataset using deep semantic image inpainting algorithm. Furthermore, we use an unsupervised domain adaptation network to detect copy-move forgery in images. Our approach can be helpful in those cases, where the c…
DomainForensics: Exposing Face Forgery across Domains via Bi-directional Adaptation
College of Computer Science and Technology, Ocean University of China（中国海洋大学计算机科学与技术学院）；School of Electronics and Computer Science, University of Southampton（南安普顿大学电子与计算机科学学院）；School of Creative Technologies, Faculty of Creative and Cultural Industries, University of Portsmouth（朴茨茅斯大学创意与文化产业学院创意技术学院）；School of Computing and Mathematic Sciences, University of Leicester（莱斯特大学计算与数学科学学院）
In recent years, image manipulation is becoming increasingly more accessible, yielding more natural-looking images, owing to the modern tools in image processing and computer vision techniques. The task of the identification of forged images has become very challenging. Amongst different types of forgeries, the cases of Copy-Move forgery are increasing manifold, due to the difficulties involved to detect this tampering. To tackle such problems, publicly available datasets are insufficient. In this paper, we propose to create a synthetic forged dataset using deep semantic image inpainting and c…
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））
Latent Spatiotemporal Adaptation for Generalized Face Forgery Video Detection
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）；RealAI
University Federico II of Naples（那不勒斯费德里科二世大学）；Technical University of Munich（慕尼黑工业大学）；University of Erlangen-Nuremberg（埃尔朗根-纽伦堡大学）
Distinguishing manipulated from real images is becoming increasingly difficult as new sophisticated image forgery approaches come out by the day. Naive classification approaches based on Convolutional Neural Networks (CNNs) show excellent performance in detecting image manipulations when they are trained on a specific forgery method. However, on examples from unseen manipulation approaches, their performance drops significantly. To address this limitation in transferability, we introduce Forensic-Transfer (FT). We devise a learning-based forensic detector which adapts well to new domains, i.e.…
Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；Beijing Key Laboratory of Advanced Information Science and Network Technology（北京先进信息科学与网络技术重点实验室）；Baidu VIS（百度视觉技术部）
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）
Enhancing Frequency Forgery Clues for Diffusion-Generated Image Detection
School of Computer and Communication Sciences, EPFL（瑞士联邦理工学院计算机与通信科学学院）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）
InfoDense: Density-Aware Regional Decisive Replay for Memory-Efficient Incremental Face Forgery Detection
Huazhong Agricultural University（华中农业大学）；Peking University（北京大学）；National University of Singapore（新加坡国立大学）；Nantong University（南通大学）；Wuhan University（武汉大学）
DOCFORGE-BENCH: A Comprehensive 0-shot Benchmark for Document Forgery Detection and Analysis
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of California, Irvine（加州大学尔湾分校）；Washington University in St. Louis（圣路易斯华盛顿大学）；Duke University（杜克大学）；University of California, Davis（加州大学戴维斯分校）；New York University（纽约大学）；Georgetown University（乔治城大学）
University of Sydney（悉尼大学）；UNSW Canberra（新南威尔士大学堪培拉分校）；CSIRO Data61（澳大利亚联邦科学与工业研究组织数据61研究所）
Forgery facial images and videos have increased the concern of digital security. It leads to the significant development of detecting forgery data recently. However, the data, especially the videos published on the Internet, are usually compressed with lossy compression algorithms such as H.264. The compressed data could significantly degrade the performance of recent detection algorithms. The existing anti-compression algorithms focus on enhancing the performance in detecting heavily compressed data but less consider the compression adaption to the data from various compression levels. We bel…
MoE-FFD: Mixture of Experts for Generalized and Parameter-Efficient Face Forgery Detection
Rapid-Rich Object Search (ROSE) Lab, School of Electrical and Electronic Engineering, Nanyang Technology University（快速丰富目标搜索（ROSE）实验室，电气与电子工程学院，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 Electrical and Engineering, City University of Hong Kong（电气与工程系，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，浙江工业大学城市学院）；Department of Computer Science, City University of Hong Kong（计算机科学系，香港城市大学）
COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Pengcheng Laboratory（鹏城实验室）；College of Computer Science and Technology, Ocean University of China（中国海洋大学计算机科学与技术学院）；University at Buffalo, SUNY（纽约州立大学布法罗分校）
SEED: Simple ViT and Evolving Harness for Explainable Text Forgery Detection
State Key Laboratory of Internet of Things for Smart City, Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系智慧城市物联网国家重点实验室）；School of Information and Software Engineering, University of Electronic Science and Technology of China（电子科技大学信息与软件工程学院）
提出SEED系统，结合相似性引导数据增强、单一ViT联合检测与定位、以及基于MLLM的演化报告生成，在ACM MM 2026文本伪造挑战赛中获得第三名。
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）
FreqBlender: Enhancing DeepFake Detection by Blending Frequency Knowledge
School of Computer Science and Technology, Ocean University of China（中国海洋大学计算机科学与技术学院）；School of Data Science, The Chinese University of Hong Kong, Shenzhen, China（香港中文大学（深圳）数据科学学院）
We propose Deep Distribution Transfer(DDT), a new transfer learning approach to address the problem of zero and few-shot transfer in the context of facial forgery detection. We examine how well a model (pre-)trained with one forgery creation method generalizes towards a previously unseen manipulation technique or different dataset. To facilitate this transfer, we introduce a new mixture model-based loss formulation that learns a multi-modal distribution, with modes corresponding to class categories of the underlying data of the source forgery method. Our core idea is to first pre-train an enco…
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）
DBCF: Dual-Branch Complementary Fusion of Foundation Models for Generalized Deepfake Detection
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Ocean University of China（中国海洋大学）
Masked Conditional Diffusion Model for Enhancing Deepfake Detection
Chengdu University of Information Technology（成都信息工程大学）；Purdue University in Indianapolis（印第安纳波利斯普渡大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；University at Albany, State University of New York (SUNY)（纽约州立大学奥尔巴尼分校）
StegaFFD: Privacy-Preserving Face Forgery Detection via Fine-Grained Steganographic Domain Lifting
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Future Technology, University of Chinese Academy of Sciences（中国科学院大学未来技术学院）；Beihang University（北京航空航天大学）；Macau University of Science and Technology（澳门科技大学）；Nanyang Technological University（南洋理工大学）
Humans comprehend a natural scene at a single glance; painters and other visual artists, through their abstract representations, stressed this capacity to the limit. The performance of computer vision solutions matched that of humans in many problems of visual recognition. In this paper we address the problem of recognizing the genre (subject) in digitized paintings using Convolutional Neural Networks (CNN) as part of the more general dealing with abstract and/or artistic representation of scenes. Initially we establish the state of the art performance by training a CNN from scratch. In the ne…
Generalized Face Forgery Detection via Adaptive Learning for Pre-trained Vision Transformer
School of Information Technology, Jiangxi University of Finance and Economics（江西财经大学信息技术学院）；School of Computer Science and Engineering, Sun Yat-Sen University（中山大学计算机科学与工程学院）；Rapid-Rich Object Search (ROSE) Lab, School of Electrical and Electronic Engineering, Nanyang Technology University（南洋理工大学电机与电子工程学院快速目标搜索实验室）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University（深圳大学大数据系统计算技术国家工程实验室）；China-Singapore International Joint Research Institute（中新国际联合研究院）
Enhancing Test Time Adaptation with Few-shot Guidance
Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University（图像通信与网络工程研究所，上海交通大学）；State Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家重点实验室，南京大学）；School of Software & Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University（软件学院及SDU-NTU联合人工智能研究中心，山东大学）；Macao Polytechnic University（澳门理工学院）
Wavelet-Driven Generalizable Framework for Deepfake Face Forgery Detection
International Institute of Information Technology Hyderabad（印度信息技术学院海得拉巴分校）；Indian Institute of Technology Hyderabad（印度理工学院海得拉巴分校）；Michigan State University（密歇根州立大学）
Person re-identification (ReID) has achieved significant improvement under the single-domain setting. However, directly exploiting a model to new domains is always faced with huge performance drop, and adapting the model to new domains without target-domain identity labels is still challenging. In this paper, we address cross-domain ReID and make contributions for both model generalization and adaptation. First, we propose Part Aligned Pooling (PAP) that brings significant improvement for cross-domain testing. Second, we design a Part Segmentation (PS) constraint over ReID feature to enhance a…
