🔎 AIGC Detection¶

🎞️ ECCV2026 · 8 paper notes

📌 Same area in other venues: 📷 CVPR2026 (10) · 🔬 ICLR2026 (30) · 💬 ACL2026 (17) · 🧪 ICML2026 (11) · 🤖 AAAI2026 (2) · 🧠 NeurIPS2025 (9)

Beyond Artifacts: Real-Centric Envelope Modeling for Reliable AI-Generated Image Detection: Instead of primarily tracking artifacts from known generators, REM learns a feature envelope of real images using controlled reconstructions and stabilizes it across degradations, achieving 84.2% balanced accuracy on RealChain with chain degradations, 18.4 percentage points above DDA.
Code-in-the-Loop Forensics: Agentic Tool Use for Image Forgery Detection: The paper proposes ForenAgent: it packs 12 low-level forensic operators (FFT, DWT, resampling periodicity, SRM, Bayar, PRNU, JPEG Ghost, and others) into a toolbox that an MLLM can call directly, lets the model generate and execute Python code over multiple turns and feed the sandboxed forensic visualizations back as observations, and trains it in two stages with cold start plus a GRPO stage driven by four process rewards — raising overall accuracy on the self-built 100k-image three-class FABench from 81.2% (best baseline) to 89.5%, and reaching 83.1% on SIDA-Test for the best cross-dataset generalization.
SPLIT: Training-Free AI-Generated and Partially Edited Video Detection via Spatial Patch-Level Incoherence and Temporal Roughness: SPLIT is a fully training-free detector that computes two statistics over the patch tokens of a frozen vision encoder — Two-step Temporal Roughness (TTR, comparing accumulated 1-step and 2-step feature displacements) and Local Spatial Motion Incoherence (LSMI, the spatial gradient of a feature-space motion field) — fuses them multiplicatively with gamma correction, and calibrates its threshold on real videos only, reaching the highest Fake Recall at FPR = 0.1% on all three benchmarks (74.45% on FakeParts, far above the best supervised baseline ReStraV at 35.67%).
From Masks to Pixels and Meaning: A New Taxonomy, Benchmark and Metrics for VLM Image Tampering: Existing tampering benchmarks annotate "where the edit is" with object masks, which are badly misaligned with the true generative edit signal; this paper reformulates VLM image tampering as a pixel-grounded, semantics- and language-aware task, releases PIXAR — 420K image pairs with a taxonomy of 8 edit primitives, thresholded pixel labels \(M_\tau\) and semantic class labels — together with a five-loss training framework and pixel/semantic/language metrics, raising tamper-localization IoU from 11.8% to 18.1% under the same backbone.
Efficient Document Tampering Localization with Multi-Level Discrepancy Features and Unified DCT-Quantization Embedding: DiffNet introduces two complementary designs: a multi-level discrepancy transform that converts the feature pyramid from content signals into sign-invariant discrepancy intensity signals, and a unified DCT-quantization joint embedding that replaces the traditional, high-overhead Frequency Perception Head (FPH) with discrete embeddings. This achieves approximately a 30% F1 improvement on cross-domain and human-manipulated document localization at a lower computational cost, with up to a 7-fold increase in throughput.
Trustworthy Image Authentication using Forensic Knowledge Graphs: This work combines the "evidence-seeking" capability of image forensics with the natural language generation of VLMs into a single system: first, a self-supervised forensic fingerprint backbone is used to segment the image into forensically consistent regions and predict the source identity, post-processing history, and compression parameters for each region to construct a structured "Forensic Knowledge Graph (FKG)"; subsequently, a VLM is guided to generate step-by-step verifiable forensic explanations based strictly on the graph, consistently outperforming pure forensic models and pure VLMs in terms of detection, forgery typing/localization, and forensic explanation.
Asymmetric Anchoring: Opening the Black Box of MLLMs for Forgery Detection: AAP aligns intermediate multimodal representations with a frozen visual reference only for real images, then uses patchwise alignment errors to guide localization, achieving 97.1% detection accuracy and 56.8% localization IoU with AAP-lisa on SID-Set.
Q-REAL: Towards Naturalness and Distortion Evaluation for AI-Generated Content: To overcome the uninterpretable single-scalar score paradigm in text-to-image quality assessment, this work establishes Q-Real—a 10K-image fine-grained benchmark with 32.8K entities annotated across Naturalness and Distortion dimensions—alongside a progressive three-stage MLLM training pipeline that achieves entity-level judgment, spatial grounding with causal attribution, and consistent quality score prediction.