Digital Media Forensic Detection

Latest papers

• Typographic Attack Against VLM-based AI-generated Image Detection

  Eunmin Lee, Jungwoo Kim, Jong-Seok Lee · 1 October 2026

  Vision-language models (VLMs) are increasingly used for AI-generated image (AIGI) detection, providing natural-language explanations for authenticity judgments. However, their ability to interpret text within images may also expose these judgments to misleading semantic cues. We systematically evalu…

• A Generalizable and Explainable Framework for Synthetic Video Detection Using First-Digit Gradient Statistics

  Sidharth Shanu, Gautam Kumar, Tej Singh · 1 October 2026

  AI video generators have not only become harder to detect but are used to generate a diverse set of scenarios from landscapes to street views to animal videos. This creates a problem where CNN-based detectors are effective but offer no insight into their inner workings, while forensics-based detecto…

• Agentic Tool-Augmented Reasoning for Explainable Image Forgery Detection

  Zhiya Tan, Jing Huang, Changtao Miao, Lin Tan, Xin Zhang, Weiwei Feng, Jianshu Li, Joey Tianyi Zhou · 1 October 2026

  Conventional image forgery detection methods produce binary scores or pixel-level masks without interpretable evidence, while recent multimodal large language model (MLLM)-based approaches generate post-hoc explanations of predetermined classification results rather than reasoning from evidence. Ins…

• Team MSU GenText-Forensics Challenge 2026 Technical Report

  Kirill Koltsov, Aleksandr Gushchin, Dmitriy Vatolin, Anastasia Antsiferova · 1 October 2026

  Document text forgery has evolved beyond simple pixel-level manipulation: modern attacks alter not only the appearance of a document but also its meaning, and increasingly target the OCR & LLM pipelines that consume such documents. The ACM MM 2026 GenText-Forensics challenge therefore requires syste…

• Forensic-Aware Continual Adaptation for Image Forgery Localization

  Chenqi Kong, Song Xia, Anwei Luo, Peisong He, Alex C. Kot, Yuming Fang · 1 October 2026

  The rapid evolution of image manipulation techniques has raised growing public security concerns. Existing Image Forgery Localization (IFL) methods can accurately localize manipulated regions but are often unable to adapt to newly emerging forgeries. In real-world forensic scenarios, data typically …

• ModalFidelity: Routing Modalities for Deepfake Detection on a Budget

  Oguzhan Baser, Kaan Kale, Sriram Vishwanath, Sandeep Chinchali · 1 October 2026

  Deepfakes no longer need to fake a whole video. Generators that read the transcript now alter only the few seconds in which a video's meaning turns, so a forgery hides in a small, unknown fraction of the video. Yet detectors still read every one-second window of both the audio and image streams, spe…

• A Benchmark & Dataset for Detecting AI-Manipulated Visual Evidence in the Court System

  Kelly McConvey, Sajad Ebrahimi, Nima Jamali, Jalehsadat Mahdavimoghaddam, Matina Mahdizadeh Sani, Maksym Taranukhin, Wentao Zhang, Jacquelyn Burkell, Yuntian Deng, Karen Eltis, Maura R. Grossman, Vered Shwartz, Ebrahim Bagheri · 30 September 2026

  Photographic evidence is becoming increasingly vulnerable to forms of alteration and fabrication that existing legal and technical workflows are not well equipped to evaluate. Surveillance frames, dashcam stills, and phone photographs may be used to establish presence, sequence, causation, damage, o…

• REALIS: A Curated Dataset for Studying the Challenges of AI Image Detection

  Aleksandr Gushchin, Khaled Abud, Georgii Bychkov, Ekaterina Shumitskaya, Artem Filippov, Sergey Lavrushkin, Dmitriy S. Vatolin, Anastasia Antsiferova · 29 September 2026

  AI-generated image detectors are often evaluated on benchmarks where real and synthetic images differ in content, quality, or generation artifacts, allowing models to rely on dataset-specific cues and fail on unfamiliar generators or processed images. Existing datasets provide limited support for ev…

• Frequency-Domain AI-Generated Image Detection: Exploring Decoder and Channel Attention for Feature Refinement

  Uday Shankar Roy, Mahbuba Jahan Minu · 29 September 2026

  With the rapid progress of AI, the number of AI-generated images has increased significantly in recent years. However, the increasing variety of image generation models makes detection more difficult. In this work, we use Fast Fourier Transform (FFT) representation with EfficientNet-B0 for AI-genera…

• Cross-modal Translation via Conditional Latent Denoising for Video Deepfake Detection

  Xinzhe Li, Youzhi Tu, Kong Aik Lee · 29 September 2026

  The growing threat of video deepfakes necessitates multimodal detection. Beyond serving as independent indicators of authenticity, audio and visual signals have intrinsic dependencies that also provide an essential criterion for detection. Previous methods often overlook the cross-modal corresponden…

• Mandela-Bench: Multimodal Models Remember Canonical Images Instead of Seeing Them

  Yicheng Bao, Zhenkun Gao, Xiahui Guo, Mingqian Yang, Xueheng Li, Bangwei Liu, Mingang Chen, Lijun Li, Xuhong Wang, Xin Tan · 29 September 2026

  Historical photographs and other canonical images can now be edited seamlessly with a single instruction, often leaving no reliable pixel-level trace. In such cases, the only evidence of manipulation may be a fact about what the image depicts. Existing benchmarks instead rely on generator artefacts,…

• Forensic Twins: Self-Supervised Residual Learning for AI-Generated Image Forensics

  Javier Mu\~noz-Haro, Ruben Tolosana, Ruben Vera-Rodriguez, Aythami Morales, Julian Fierrez · 28 September 2026

  Detectors of AI-generated images are typically trained using samples from all Generative AI architectures they must catch, and struggle as soon as a new architecture emerges. Recent approaches have explored self-supervised pre-training as an alternative solution, yet standard frameworks work against…

• ManiVid: Unified and Explainable Forensic Analysis of Manipulated Videos

  Hengrui Kang, Zhonghao Yan, Yuxuan Yang, Ruoyan Jing, Yuncheng Guo, Hao Chen, Kongming Liang, Zhanyu Ma, Conghui He, Weijia Li · 28 September 2026

  Rapid advances in AI-generated video (AIGV) have increased the risks posed by deceptive video manipulation. Unlike fully synthetic videos, manipulated videos retain most source content and alter only localized regions, making forensic analysis particularly challenging. Existing video forgery researc…

• Can Pixels Alone Reveal Image Origin? Minimax Limits and Learnable Interfaces for Passive Provenance

  Kai Yao · 28 September 2026

  Passive image provenance asks whether pixels alone can reveal where an image came from: a human, an aggregate AI class, or a particular generator. This becomes a robustness problem once a source image can be edited before the verifier sees it. We study the problem as source--target verification unde…

• FARE: Forensic Acceptance Region Estimation for Catching Bait-and-Switch Image Generators

  Kai Yao, Marc Juarez · 28 September 2026

  Modern AI image generators are increasingly deployed as opaque APIs, where customers can query the deployed service, but cannot inspect model weights or architecture. This creates a practical challenge: a provider may pass governance certification with one generator and later silently switch to a ch…

• Band-Attention Modulation Network for Robust Face Forgery Detection

  Zhida Zhang, Wenkui Yang, Xinlei Ma, Qihang Fan, Jie Cao · 25 September 2026

  Face forgery detection faces critical challenges in generalizing to unseen manipulation techniques and remaining robust under image compression, which often obscures subtle artifacts. Existing methods typically rely on fixed filters or coarse band separation, lacking the adaptability to learn task-s…

• EIB-Net: Entropy-Guided Information Bottleneck for Generalizable AI-Generated Image Detection

  Zhida Zhang, Xinlei Ma, Jie Cao · 25 September 2026

  The proliferation of photorealistic AI-generated images demands robust detection methods that generalize across diverse generative models. While existing approaches target manipulation-based forgeries with local artifacts, generation-based images (e.g., from diffusion models) lack such traces, posin…

• Copy-Move Forgery Detection and Question Answering for Remote Sensing Image

  Ze Zhang, Enyuan Zhao, Di Niu, Jie Nie, Xinyue Liang, Lei Huang · 24 September 2026

  Driven by practical demands in land resource monitoring and national defense security, this paper introduces the Remote Sensing Copy-Move Question Answering (RSCMQA) task. Unlike traditional Remote Sensing Visual Question Answering (RSVQA), RSCMQA focuses on interpreting complex tampering scenarios …

• ASAP: Visual Analytics for Identifying and Analyzing Image Patterns in AI-generated Images

  Jinbin Huang, Yuki Ueno, Chen Chen, Aditi Mishra, Bum Chul Kwon, Zhicheng Liu, Chris Bryan · 24 September 2026

  Generative image models can produce highly realistic images, raising concerns about potential misuse in creating deceptive content. Current deepfake approaches face several challenges, including limited generalizability, lack of interpretability, and poor actionability. To help address these, we pre…

• Spatiality-Frequency Domain Video Forgery Detection System Based on ResNet-LSTM-CBAM and DCT Hybrid Network

  Zihao Liao, Sheng Hong, Yu Chen · 24 September 2026

  As information technology advances, digital content has become widely adopted across diverse fields such as news broadcasting, entertainment, commerce, and forensic investiga?tion. However, the availability of sophisticated multimedia editing tools has significantly increased the risk of video and i…

• AIGC Video Detection based on the fusion of spatial-frequency-optical flow multimodal features

  S. Hong, X. Q. Wang, C. Zhang, J. C. Wang, P. X. Duan, Y. W. Wang · 23 September 2026

  The rapid evolution of generative AI (e.g., Sora, Hunyuan) makes it essential to develop effective detection strategies that can generalize across ever-evolving synthesis techniques. This study is motivated by the observation of a fundamental challenge in generative models: the inherent difficulty o…

• Dissecting Agentic Forensics: The Role of Triage, Prompting, and Evidence Arbitration in Open-World Fake Image Detection

  Xianlong Li (IMT School for Advanced Studies Lucca, Italy), Pietro Bongini (University of Siena, Italy), Niccol\'o Pancino (University of Siena, Italy), Marco Blanchini (IMT School for Advanced Studies Lucca, Italy), Benedetta Tondi (University of Siena, Italy), Mauro Barni (University of Siena, Italy) · 22 September 2026

  Image forensics is increasingly an open-world problem: manipulations range from fully synthetic images to localized edits, splicing and swapping, while most forensic detectors remain specialized to a single manipulation family. Agentic AI has recently emerged as a promising solution. In principle, s…

• DFD-Lab: A Modular Audio-Visual Deepfake Detection Pipeline

  Jan Rybarczyk, Mateusz Roszkowski, Jacek Komorowski · 22 September 2026

  Comparing audio-visual deepfake detectors requires coordinating dataset adaptation, temporal input representation, model interfaces and experimental conditions. We present DFD-Lab, a modular pipeline that separates these responsibilities while supporting shared training and evaluation workflows. We …

• AniPrO: Interpretable Anime Image Provenance Detection via Multi-Dimensional Semantic Reasoning

  Yan Liu, Baoxiang Huang, Zi'an Wang, Wenbo Xie · 22 September 2026

  As generative AI becomes increasingly used in anime-style image creation, distinguishing human-drawn, AI-inpainted, and text-to-image images is important for copyright attribution, visual provenance, and content governance. Existing AI-generated image detectors mainly target real-world photographs a…

• A Training-Free Proactive Defense Against Partial Speech Manipulation via Self-Embedding Steganography

  Yigitcan \"Ozer, Zhe Zhang, Wanying Ge, Xin Wang, Junichi Yamagishi · 21 September 2026

  Partial deepfake speech, where only limited segments of an utterance are synthesized or manipulated, poses a significant challenge to existing deepfake detection systems. As the proportion of spoofed regions decreases, passive detectors become increasingly unreliable, and accurate detection and rest…