Image manipulation localization is a critical research task, given that forged images may have a significant societal impact of various aspects. Such image manipulations can be produced using traditional image editing tools (known as "shallowfakes") or advanced artificial intelligence techniques ("deepfakes"). While numerous studies have focused on image manipulation localization on either shallowfake images or deepfake videos, few approaches address both cases. In this paper, we explore the feasibility of using a deep learning network to localize manipulations in both shallow- and deep-fake images, and proposed a solution for such purpose. To precisely differentiate between authentic and manipulated pixels, we leverage the Vision Mamba network to extract feature maps that clearly describe the boundaries between tampered and untouched regions. To further enhance this separation, we propose a novel Guided Graph Neural Network (G-GNN) module that amplifies the distinction between manipulated and authentic pixels. Our evaluation results show that our proposed method achieved higher inference accuracy compared to other state-of-the-art methods.
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…
Multimodal Fake News Detection: MFND Dataset and Shallow-Deep Multitask Learning
School of Artificial Intelligence, Hebei University of Technology（河北工业大学人工智能学院）；School of Computing and Information Technology, Great Bay University（大湾大学计算与信息学院）；Guangdong Provincial Key Laboratory of Intelligent Information Processing & Shenzhen Key Laboratory of Media Security, Shenzhen University（广东省智能信息处理重点实验室 & 深圳市媒体安全重点实验室）；Dongguan Key Laboratory for Intelligence and Information Technology（东莞智能信息科技重点实验室）
PE-Mamba: Bidirectional Selective Layer Aggregation for AI-Generated Image Detection
College of Innovation & Technology, University of Michigan-Flint（密歇根大学弗林特分校创新与技术学院）；School of Electronics and Information Engineering, Korea Aerospace University（韩国航空大学电子与信息工程学院）
FakeChain: Exposing Shallow Cues in Multi-Step Deepfake Detection
Sungkyunkwan University Department of Artificial Intelligence Suwon Gyeonggi-do South Korea；Sungkyunkwan University Computer Science \& Engineering Department Suwon Gyeonggi-do South Korea；Sungkyunkwan University
HyM-UNet: Synergizing Local Texture and Global Context via Hybrid CNN-Mamba Architecture for Medical Image Segmentation
School of Information and Communication Engineering, Beijing University of Posts and Communications (BUPT)（信息与通信工程学院，北京邮电大学）；Alibaba Cloud AI Labs（阿里云人工智能实验室）
BiSegMamba: Efficient Bidirectional Tri-Oriented Mamba for 3D Medical Image Segmentation
School of Computer Science and Engineering, Beihang University（北航计算机科学与工程学院）；State Key Laboratory of Virtual Reality Technology and Systems, Beihang University（北航虚拟现实技术与系统国家重点实验室）
IGroupSS-Mamba: Interval Group Spatial-Spectral Mamba for Hyperspectral Image Classification
Nanjing University of Information Science and Technology（南京信息工程大学）；Institute of Optics and Electronics（光学电子研究所）；China University of Geosciences（中国地质大学）；University of Extremadura（埃斯特雷马杜拉大学）
An Efficient and Mixed Heterogeneous Model for Image Restoration
MAC Lab, Xiamen University, China（厦门大学）；National University of Singapore, Singapore（新加坡国立大学）；Smart City Research Institute, China Electronics Technology Group Corporation, China（中国电子科技集团有限公司智慧城市研究院）
Microsoft Research Asia（微软亚洲研究院）；Peking University（北京大学）
Existing deepfake detection methods have reported promising in-distribution results, by accessing published large-scale dataset. However, due to the non-smooth synthesis method, the fake samples in this dataset may expose obvious artifacts (e.g., stark visual contrast, non-smooth boundary), which were heavily relied on by most of the frame-level detection methods above. As these artifacts do not come up in real media forgeries, the above methods can suffer from a large degradation when applied to fake images that close to reality. To improve the robustness for high-realism fake data, we propos…
The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection
College of Computer and Mathematics, Central South University of Forestry and Technology（计算机与数学学院，中央南大学林业科技学院）；College of Computer Science and Electronic Engineering, Hunan University（计算机科学与电子工程学院，湖南大学）；College of Economics and Management, Central South University of Forestry and Technology（经济管理学院，中央南大学林业科技学院）；Department of Computer Science, State University of New York（计算机科学系，纽约州立大学）
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…
