The paper explores how partial C2PA-based Content Credentials (CCs) can be used to improve image forensics. It proposes a two-step method: first reconstructing the last operations applied to an image by analyzing its partial CCs, and second adapting forensic detectors to the image’s statistical characteristics based on that reconstruction. Experiments on commercial C2PA-enabled tools demonstrate the feasibility of this reconstruction and show significant performance gains in synthetic image detection after post‑processing.
arXiv:2609.37783v1 Announce Type: cross Abstract: Photographic evidence is becoming increasingly vulnerable to forms of alteration and fabrication that existing legal and technical workflows are not...
The paper evaluates whether multimodal large language models (MLLMs) can serve as forensic detectors of deepfake facial images. Six MLLMs—including GPT-4o, GPT-5, GPT-5.2, Gemini 2.5, Gemini 3, and Seed 1.6—were tested on a dataset of 400 images (50 authentic and 350 synthetic) generated by seven leading image models. Results show that several MLLMs achieve high detection accuracy, with GPT-5.2 reaching 97.9% in pairwise comparisons, but the presence of invisible SynthID watermarks can reduce detection performance by about 10% for Gemini.
BG-REAL is a public benchmark for detecting and localizing background manipulations in images, built from Open Images V7 with 7,000 processed samples across 1,200 source groups. It includes 6,000 real-data anchored samples, 1,000 synthetic controls, six edit families, matched authentic controls, and extensive quality‑control measures. The release offers a construction pipeline, evaluation protocol, and three external baseline results, highlighting false‑positive rates due to re‑encoding artifacts.
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, das...
arXiv:2604.25213v2 Announce Type: replace Abstract: OpenAI's GPT-Image-2 has effectively erased the visual boundary between authentic and AI-edited document images: a single number on a receipt can b...
The paper proposes a unified forensics framework that extends traditional binary image manipulation detection to a multiclass setting—distinguishing real, fully synthetic, and tampered images. It adds a segmentation branch for pixel‑level localization of tampered regions, achieving higher classification accuracy and IoU scores compared to recent benchmarks. The authors provide the implementation on GitHub for reproducibility.
arXiv:2607. 26641v1 Announce Type: cross Abstract: Identity document (ID) authentication relies on the structural integrity of complex, high-frequency security patterns.
arXiv:2609.38391v1 Announce Type: new Abstract: Document text forgery has evolved beyond simple pixel-level manipulation: modern attacks alter not only the appearance of a document but also its meani...
The paper introduces an evidence‑guided detector‑localizer‑reasoner system for text‑centric image forensics, addressing the challenges posed by AI‑generated content. It combines an image‑level authenticity detector, a localizer that extracts tampered regions, and an MLLM‑based reasoner that generates structured forensic reports grounded in the detected evidence. The system employs iterative difficulty‑aware mining and report‑mask consistency post‑processing, achieving a score of 0.638 and ranking second in the ACM Multimedia 2026 GenText‑Forensics Challenge.
FUSED is a new framework that jointly detects and localizes AI-generated inpainting by combining low-level forensic cues with high-level semantic features through a sparsely-gated Mixture-of-Experts architecture. It predicts both an image-level manipulation score and a pixel-level mask of the inpainted region. On the OpenSDID cross-generator benchmark, FUSED outperforms existing methods, especially on unseen generators, and transfers effectively to the AutoSplice and CocoGlide benchmarks, doubling localization performance.
ManiVid introduces a unified forensic analysis framework for manipulated videos, combining forgery detection, artifact grounding, and anomaly explanation. The authors release ManiVid-38K, a large dataset of 19K real‑fake video pairs with authenticity labels, forgery masks, and explanations, and a benchmark ManiVidBench with 1K balanced pairs. ManiVidLens, the proposed model, outperforms existing methods in artifact grounding and anomaly explanation while matching state‑of‑the‑art detection accuracy.
arXiv:2609.38251v1 Announce Type: cross Abstract: The rapid evolution of image manipulation techniques has raised growing public security concerns. Existing Image Forgery Localization (IFL) methods c...