Explainable Forensics of Manipulated Segments in Untrimmed Long Videos
MoE Key Laboratory of Brain-Machine Intelligence Technology, College of Artificial Intelligence, Nanjing University of Aeronautics（脑机智能技术关键实验室、人工智能学院、南京航空航天大学）；Dalian University of Technology（大连理工大学）；Nanjing University（南京大学）；National University of Singapore（新加坡国立大学）
The rapid advancement of AI-driven video generation has transformed content creation, while simultaneously increasing the risk of misinformation through localized manipulations in long-form videos. Existing video forensic methods predominantly operate on short, independent clips, and thus fail to capture realistic scenarios where AI-generated content is sparsely embedded within otherwise authentic footage. To bridge this gap, we formulate the task of Temporal AI-Generated Segment Localization and Explanation, which targets authenticity detection, temporal localization, and interpretable analysis of manipulated segments in untrimmed long videos. We further introduce TASLE, a large-scale benchmark comprising 12,472 untrimmed videos with diverse manipulation patterns and rich annotation signals, including temporal boundaries, authenticity labels, and segment-level rationales. In addition, we propose MSLoc, a coarse-to-fine forensic baseline that combines a boundary-sensitive proposal generation module for efficient long-video scanning with an MLLM-based refinement module for precise boundary localization and interpretable reasoning. Experiments validate the effectiveness of the proposed baseline, highlighting the importance of segment-level explainable forensics for long-form AI-generated video analysis. Our dataset and code are publicly available at https://debby-0527.github.io/TASLE.
Due to the increasing availability and functionality of image editing tools, many forensic techniques such as digital image authentication, source identification and tamper detection are important for forensic image analysis. In this paper, we describe a machine learning based system to address the forensic analysis of scanner devices. The proposed system uses deep-learning to automatically learn the intrinsic features from various scanned images. Our experimental results show that high accuracy can be achieved for source scanner identification. The proposed system can also generate a reliabil…
In this paper, we propose a novel CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection. Recent rapid advances in image manipulation tools and deep image synthesis techniques, such as Generative Adversarial Networks (GANs) have easily generated fake images, so detecting manipulated images has become an urgent issue. Most state-of-the-art forgery detection methods assume that images include checkerboard artifacts which are generated by using DNNs. Accordingly, we propose a novel CycleGAN without any checkerboard artifacts for counter-forensics of fake-mage detect…
Universitá di Siena（锡耶纳大学）；Politecnico di Milano（米兰理工大学）
Due to the reduction of technological costs and the increase of satellites launches, satellite images are becoming more popular and easier to obtain. Besides serving benevolent purposes, satellite data can also be used for malicious reasons such as misinformation. As a matter of fact, satellite images can be easily manipulated relying on general image editing tools. Moreover, with the surge of Deep Neural Networks (DNNs) that can generate realistic synthetic imagery belonging to various domains, additional threats related to the diffusion of synthetically generated satellite images are emergin…
Imperial College London（帝国理工学院）；University of Oulu（奥卢大学）
Recently, Generative Adversarial Networks (GANs) and image manipulating methods are becoming more powerful and can produce highly realistic face images beyond human recognition which have raised significant concerns regarding the authenticity of digital media. Although there have been some prior works that tackle face forensic classification problem, it is not trivial to estimate edited locations from classification predictions. In this paper, we propose, to the best of our knowledge, the first rigorous face forensic localization dataset, which consists of genuine, generated, and manipulated f…
Video and Image Processing Lab (VIPER), School of Electrical and Computer Engineering, Purdue University（普渡大学电气与计算机工程学院视频与图像处理实验室）；Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano（米兰理工大学电子、信息与生物工程系）
The unprecedented ease and ability to manipulate video content has led to a rapid spread of manipulated media. The availability of video editing tools greatly increased in recent years, allowing one to easily generate photo-realistic alterations. Such manipulations can leave traces in the metadata embedded in video files. This metadata information can be used to determine video manipulations, brand of video recording device, the type of video editing tool, and other important evidence. In this paper, we focus on the metadata contained in the popular MP4 video wrapper/container. We describe our…
ManiVid: Unified and Explainable Forensic Analysis of Manipulated Videos
Shanghai Jiao Tong University（上海交通大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Fudan University（复旦大学）；The Chinese University of Hong Kong（香港中文大学）；Shanghai AI Laboratory（上海人工智能实验室）；Tsinghua University（清华大学）
Manipulated videos often contain subtle inconsistencies between their visual and audio signals. We propose a video forensics method, based on anomaly detection, that can identify these inconsistencies, and that can be trained solely using real, unlabeled data. We train an autoregressive model to generate sequences of audio-visual features, using feature sets that capture the temporal synchronization between video frames and sound. At test time, we then flag videos that the model assigns low probability. Despite being trained entirely on real videos, our model obtains strong performance on the …
Video Forgery Detection for Surveillance Cameras: A Review
Kurdistan Technical Institute（库尔德斯坦技术学院）；University of Kurdistan Hewlêr（库尔德斯坦赫威尔大学）；University of Raparin（拉帕林大学）；Charmo University（恰尔莫大学）；Tishk International University（蒂什克国际大学）；Maharaja Sriram Chandra Bhanja Deo University（马哈拉贾·斯里拉姆·钱德拉·班贾·德奥大学）
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.…
University of Notre Dame（圣母大学）；University of Campinas（坎皮纳斯大学）
A dramatic rise in the flow of manipulated image content on the Internet has led to an aggressive response from the media forensics research community. New efforts have incorporated increased usage of techniques from computer vision and machine learning to detect and profile the space of image manipulations. This paper addresses Image Provenance Analysis, which aims at discovering relationships among different manipulated image versions that share content. One of the main sub-problems for provenance analysis that has not yet been addressed directly is the edit ordering of images that share ful…
In this paper, we address a new image forensics task, namely the detection of fake flood images generated by ClimateGAN architecture. We do so by proposing a hybrid deep learning architecture including both a detection and a localization branch, the latter being devoted to the identification of the image regions manipulated by ClimateGAN. Even if our goal is the detection of fake flood images, in fact, we found that adding a localization branch helps the network to focus on the most relevant image regions with significant improvements in terms of generalization capabilities and robustness agai…
Digital Image Forgery Detection Using Transfer Learning
Marmara University, Department of Computer Engineering（马尔马拉大学计算机工程系）；Loyola University Chicago, Department of Computer Science（芝加哥洛伊拉大学计算机科学系）；Biruni University, Department of Computer Engineering（比鲁尼大学计算机工程系）
SOKENDAI (The Graduate University for Advanced Studies)（综合研究大学院大学）；National Institute of Informatics（国立信息学研究所）；The University of Edinburgh（爱丁堡大学）
Detecting manipulated images and videos is an important topic in digital media forensics. Most detection methods use binary classification to determine the probability of a query being manipulated. Another important topic is locating manipulated regions (i.e., performing segmentation), which are mostly created by three commonly used attacks: removal, copy-move, and splicing. We have designed a convolutional neural network that uses the multi-task learning approach to simultaneously detect manipulated images and videos and locate the manipulated regions for each query. Information gained by per…
ETH Zurich（苏黎世联邦理工学院）；Beijing Institute of Technology（北京理工大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）
On existing public benchmarks, face forgery detection techniques have achieved great success. However, when used in multi-person videos, which often contain many people active in the scene with only a small subset having been manipulated, their performance remains far from being satisfactory. To take face forgery detection to a new level, we construct a novel large-scale dataset, called FFIW-10K, which comprises 10,000 high-quality forgery videos, with an average of three human faces in each frame. The manipulation procedure is fully automatic, controlled by a domain-adversarial quality assess…
FACT: A Forensic Agent with Compiled Tool-Use Trajectories for AI-Generated Image Detection
Lingnan University（岭南大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；The University of Hong Kong（香港大学）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen University（深圳大学）
Propose and Rectify: A Forensics-Driven MLLM Framework for Image Manipulation Localization
Department of Electrical Engineering, City University of Hong Kong（香港城市大学电子工程系）；Rapid-Rich Object Search (ROSE) Lab, School of Electrical and Electronic Engineering, Nanyang Technology University（南洋理工大学电子与电气工程学院快速丰富对象搜索（ROSE）实验室）；Shanghai Jiao Tong University（上海交通大学）
Generating Attribution Reports for Manipulated Facial Images: A Dataset and Baseline
Xi’an Jiaotong University（西安交通大学）；Hefei University of Technology（合肥工业大学）；CSIRO（澳大利亚联邦科学与工业研究组织）；Northwestern Polytechnical University（西北工业大学）；University of Macau（澳门大学）
East China Normal University（华东师范大学）；Shanghai Industrial Control Safety Innovation Technology Co., LTD（上海工业控制安全创新科技有限公司）；Alibaba Group（阿里巴巴集团）；Nanyang Technological University（南洋理工大学）；Zhejiang Sci-Tech University（浙江理工大学）
Full face synthesis and partial face manipulation by virtue of the generative adversarial networks (GANs) and its variants have raised wide public concerns. In the multi-media forensics area, detecting and ultimately locating the image forgery has become an imperative task. In this work, we investigate the architecture of existing GAN-based face manipulation methods and observe that the imperfection of upsampling methods therewithin could be served as an important asset for GAN-synthesized fake image detection and forgery localization. Based on this basic observation, we have proposed a novel …
FraudBench: A Multimodal Benchmark for Detecting AI-Generated Fraudulent Refund Evidence
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）；Alibaba-NTU Global e-Sustainability CorpLab (ANGEL)（阿里巴巴-NTU全球可持续性科技实验室）；Alibaba Group（阿里巴巴集团）
SenseTime Research（商汤科技研究院）；Beijing University of Posts and Telecommunications（北京邮电大学）；Shanghai AI Laboratory（上海人工智能实验室）；Beihang University（北京航空航天大学）；University of Science and Technology of China（中国科学技术大学）；Nanyang Technological University（南洋理工大学）
The rapid progress of photorealistic synthesis techniques has reached at a critical point where the boundary between real and manipulated images starts to blur. Thus, benchmarking and advancing digital forgery analysis have become a pressing issue. However, existing face forgery datasets either have limited diversity or only support coarse-grained analysis. To counter this emerging threat, we construct the ForgeryNet dataset, an extremely large face forgery dataset with unified annotations in image- and video-level data across four tasks: 1) Image Forgery Classification, including two-way (rea…
Shahjalal University of Science and Technology（沙赫贾拉尔科技大学）；University of Alberta（阿尔伯塔大学）；Fordham University（福特汉姆大学）
Forensic analysis of manipulated pixels requires the identification of various hidden and subtle features from images. Conventional image recognition models generally fail at this task because they are biased and more attentive toward the dominant local and spatial features. In this paper, we propose a novel Gated Context Attention Network (GCA-Net) that utilizes non-local attention in conjunction with a gating mechanism in order to capture the finer image discrepancies and better identify forged regions. The proposed framework uses high dimensional embeddings to filter and aggregate the relev…
Progressive Decision-Making for Localizing Open-Ended AI-Generated Image Forgeries
School of Artificial Intelligence, University of Science and Technology Beijing（北京科技大学人工智能学院）；Institute of Artificial Intelligence, University of Science and Technology Beijing（北京科技大学人工智能研究院）；Key Laboratory of Intelligent Bionic Unmanned Systems, Ministry of Education, University of Science and Technology Beijing（北京科技大学智能仿生无人系统教育部重点实验室）
