🔎 AIGC Detection

🎞️ ECCV2026 · 3 paper notes

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

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.

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.