Recently, reconstruction-based methods have gained attention for AIGC image detection. These methods leverage pre-trained diffusion models to reconstruct inputs and measure residuals for distinguishing real from fake images. Their key advantage lies in reducing reliance on dataset-specific artifacts and improving generalization under distribution shifts. However, they are limited by significant inefficiency due to multi-step inversion and reconstruction, and their reliance on diffusion backbones further limits generalization to other generative paradigms such as GANs.
In this paper, we propose a novel fake image detection framework, called R$^2$BD, built upon two key designs: (1) G-LDM, a unified reconstruction model that simulates the generation behaviors of VAEs, GANs, and diffusion models, thereby broadening the detection scope beyond prior diffusion-only approaches; and (2) a residual bias calculation module that distinguishes real and fake images in a single inference step, which is a significant efficiency improvement over existing methods that typically require 20$+$ steps.
Extensive experiments on the benchmark from 10 public datasets demonstrate that R$^2$BD is over 22$\times$ faster than existing reconstruction-based methods while achieving superior detection accuracy. In cross-dataset evaluations, it outperforms state-of-the-art methods by an average of 13.87\%, showing strong efficiency and generalization across diverse generative methods. The code and dataset used for evaluation are available at https://github.com/QingyuLiu/RRBD.
East China Normal University（华东师范大学）；Alibaba Group（阿里巴巴集团）；Nanyang Technological University（南洋理工大学）；Kyushu University（九州大学）；Zhejiang University（浙江大学）
At this moment, GAN-based image generation methods are still imperfect, whose upsampling design has limitations in leaving some certain artifact patterns in the synthesized image. Such artifact patterns can be easily exploited (by recent methods) for difference detection of real and GAN-synthesized images. However, the existing detection methods put much emphasis on the artifact patterns, which can become futile if such artifact patterns were reduced. Towards reducing the artifacts in the synthesized images, in this paper, we devise a simple yet powerful approach termed FakePolisher that perfo…
The University of Hong Kong（香港大学）；Tencent Games（腾讯游戏）；Texas A&M University（得克萨斯农工大学）
We introduce SparseNeuS, a novel neural rendering based method for the task of surface reconstruction from multi-view images. This task becomes more difficult when only sparse images are provided as input, a scenario where existing neural reconstruction approaches usually produce incomplete or distorted results. Moreover, their inability of generalizing to unseen new scenes impedes their application in practice. Contrarily, SparseNeuS can generalize to new scenes and work well with sparse images (as few as 2 or 3). SparseNeuS adopts signed distance function (SDF) as the surface representation,…
Generalizable Synthetic Image Detection via Language-guided Contrastive Learning
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；State Key Laboratory of Internet of Things for Smart City（智慧城市物联网国家重点实验室）；Department of Computer and Information Science, Faculty of Science and Technology, University of Macau（澳门大学计算机与信息科学系）；Faculty of Applied Sciences, Macao Polytechnic University（澳门 polytechnic 大学应用科学系）
MFVLR: Multi-domain Fine-grained Vision-Language Reconstruction for Generalizable Diffusion Face Forgery Detection and Localization
Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences)（计算机科学与技术学院，齐鲁工业大学（山东省科学院））；School of Computing, National University of Singapore（国立新加坡大学计算机学院）；Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences)（山东省人工智能研究院，齐鲁工业大学（山东省科学院））；Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology（教育部计算机视觉与系统重点实验室，天津工业大学）
Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstruction
Institute of Science and Technology for Brain-inspired Intelligence（脑启发智能科学与技术研究院）；Fudan University（复旦大学）；School of Biomedical Engineering（生物医学工程学院）；Southern Medical University（南方医科大学）；School of Computer Science（计算机科学学院）；Xi’an Jiaotong University（西安交通大学）；School of Life Science and Technology（生命科学与技术学院）；MOE Frontiers Center for Brain Science（教育部脑科学前沿中心）；Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Ministry of Education)（教育部计算神经科学与脑启发智能重点实验室）；State Key Laboratory of Brain Function and Disorders（脑功能与疾病国家重点实验室）
Generalizable Holographic Reconstruction via Amplitude-Only Diffusion Priors
Department of Bio and Brain Engineering（生物与脑工程系）；Department of Mechanical Engineering（机械工程系）；Kim Jaechul Graduate School of AI（Kim Jaechul人工智能研究生院）；KAIST Institute for Health Science and Technology（KAIST健康科学与技术研究所）
Pohang University of Science and Technology（浦项科技大学）；Department of Computer Science and Engineering（计算机科学与工程系）；Engineering Pohang University of Science（浦项科技大学工程学院（注：原文本可能存在排版问题，更准确的应为浦项科技大学计算机科学与工程系））
Deep neural advancements have recently brought remarkable image synthesis performance to the field of image inpainting. The adaptation of generative adversarial networks (GAN) in particular has accelerated significant progress in high-quality image reconstruction. However, although many notable GAN-based networks have been proposed for image inpainting, still pixel artifacts or color inconsistency occur in synthesized images during the generation process, which are usually called fake textures. To reduce pixel inconsistency disorder resulted from fake textures, we introduce a GAN-based model u…
NS-Net: Decoupling CLIP Semantic Information through NULL-Space for Generalizable AI-Generated Image Detection
Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology（数字取证工程研究中心，教育部，南京信息科技大学）；University of Macau（澳门大学）
DEFUSE: Generalizable Backdoor Defense for Self-Supervised Encoders with Generative Priors
Ant Group（蚂蚁集团）；Purple Mountain Laboratories（紫金山实验室）；City University of Hong Kong（香港城市大学）；Beijing Institute of Technology（北京理工大学）；Alibaba Group（阿里巴巴集团）
Vulnerability-Aware Spatio-Temporal Learning for Generalizable Deepfake Video Detection
CVI 2 , SnT, University of Luxembourg（CVI 2 ，SnT，卢森堡大学）；Cristal Laboratory, National School of Computer Sciences, University of Manouba（Cristal实验室，国家计算机科学学校，曼努巴大学）
Seeing Before Reasoning: A Unified Framework for Generalizable and Explainable Fake Image Detection
Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen Key Laboratory of Media Security, and SZU-AFS Joint Innovation Center for AI Technology（广东省智能信息处理重点实验室、深圳媒体安全重点实验室及深圳大学-AFS联合人工智能技术创新中心）；Tencent Youtu Lab（腾讯优图实验室）；Peking University（北京大学）；Sun Yat-Sen University（中山大学）
A Sanity Check for Multi-In-Domain Face Forgery Detection in the Real World
Peking University（北京大学）；Nanjing University（南京大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Wuhan University（武汉大学）；Stanford University（斯坦福大学）
HyperDet: Generalizable Detection of Synthesized Images by Generating and Merging A Mixture of Hyper LoRAs
Zhejiang University（浙江大学）；Jinan University（暨南大学）；Imperial College London（伦敦帝国理工学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Nanjing University of Information Science and Technology（南京信息工程大学）
EvGGS: A Collaborative Learning Framework for Event-based Generalizable Gaussian Splatting
Function Hub, Hong Kong University of Science and Technology（香港科技大学（广州）功能中心）；Faculty of Robot Science and Engineering, Northeastern University（东北大学机器人科学与工程学院）
Face anti-spoofing has drawn a lot of attention due to the high security requirements in biometric authentication systems. Bringing face biometric to commercial hardware became mostly dependent on developing reliable methods for detecting fake login sessions without specialized sensors. Current CNN-based method perform well on the domains they were trained for, but often show poor generalization on previously unseen datasets. In this paper we describe a method for utilizing unsupervised pretraining for improving performance across multiple datasets without any adaptation, introduce the Entry A…
ShanghaiTech University（上海科技大学）；University of California, San Diego（加利福尼亚大学圣迭戈分校）；Adobe Research（Adobe研究院）
We present MVSNeRF, a novel neural rendering approach that can efficiently reconstruct neural radiance fields for view synthesis. Unlike prior works on neural radiance fields that consider per-scene optimization on densely captured images, we propose a generic deep neural network that can reconstruct radiance fields from only three nearby input views via fast network inference. Our approach leverages plane-swept cost volumes (widely used in multi-view stereo) for geometry-aware scene reasoning, and combines this with physically based volume rendering for neural radiance field reconstruction. W…
REArtGS++: Generalizable Articulation Reconstruction with Temporal Geometry Constraint via Planar Gaussian Splatting
Hefei Institutes of Physical Science, Chinese Academy of Sciences（合肥物理科学研究院，中国科学院）；University of Science and Technology of China（中国科学技术大学）；Hefei University of Technology（合肥工业大学）；RoboticsX, Tencent（腾讯机器人院）；Shanghai Jiao Tong University（上海交通大学）
Can We Build a Monolithic Model for Fake Image Detection? SICA: Semantic-Induced Constrained Adaptation for Unified-Yet-Discriminative Artifact Feature Space Reconstruction
Sichuan University（四川大学）；The Hong Kong University of Science and Technology（香港科学与技术大学）；University of Science and Technology of China（中国科学技术大学）；South China University of Technology（华南理工大学）
Stereo-GS: Multi-View Stereo Vision Model for Generalizable 3D Gaussian Splatting Reconstruction
Department of Computer Science, Hong Kong Baptist University（香港 Baptist 大学计算机科学系）；NVIDIA AI Technology Center, NVIDIA（NVIDIA 人工智能技术中心）；School of Control Science and Engineering, Shandong University（控制科学与工程学院，山东大学）
Explicit Correlation Learning for Generalizable Cross-Modal Deepfake Detection
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；University at Buffalo, State University of New York（纽约州立大学布法罗分校）；Institute of Microelectronics, Chinese Academy of Sciences（中国科学院微电子研究所）
SaLon3R: Structure-aware Long-term Generalizable 3D Reconstruction from Unposed Images
The Chinese University of Hong Kong（香港中文大学）；Hong Kong Center for Logistics Robotics（香港物流机器人中心）；University of California, Berkeley（加州大学伯克利分校）；Zhejiang University（浙江大学）
