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

Paper recorded by Signals 4 on 2026-09-25 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-25 on arXiv · recorded by Signals 4 on 2026-09-28

Category: cs.CV · 计算机视觉 · first seen 2026-09-28

Abstract

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 the forensic task, e.g., their augmentations overwrite the micro-statistics of image formation. Thi

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#22 most recent of 342 cs.CV papers we have recorded · ↑ newer: Structured Reasoning Agentic Framework for Interpretable Critical View · ↓ older: SatNav: A Scalable Benchmark for Long-Horizon UAV Vision-Language Navi
Cite this page: Forensic Twins: Self-Supervised Residual Learning for AI-Generated Image Forensics: the #22 most recent of 342 cs.CV papers we have recorded (as of 2026-09-25). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/forensic-twins-self-supervised-residual-learning-for-ai-generated-image-forensic.html
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