EvoKnow is a replay‑free framework for continual AI‑generated image detection that treats forensic knowledge as evolving. It keeps a shared forensic basis from base domains, adds isolated residual experts for new generators, and uses an Analytical Incremental Router (AIR) updated in closed form from current‑stage data and accumulated statistics. Experiments show strong cross‑generator generalization, few‑shot expansion, and low forgetting, achieving 96.70% accuracy on non‑base generators and 96.32% mean stage‑wise accuracy under a strict replay‑free protocol.
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...
The paper introduces RED (Reconstruction Evolution Dynamics), a new framework for detecting AI-generated images that leverages the evolution of intermediate reconstruction stages rather than relying solely on static representations or endpoint discrepancies. RED uses a frozen multiscale VQ‑VAE and a frozen CLIP encoder to capture a reconstruction trajectory, then learns image‑adaptive stage weights from token negative log‑likelihoods provided by a frozen VAR model. Experiments on six benchmarks show RED achieves the highest average accuracy (92.5%) and precision (97.5%) among evaluated methods, and it remains robust to common image degradations.
arXiv:2609.05876v1 Announce Type: cross Abstract: AI-generated image detection is increasingly open-world: new image generators produce highly realistic images that make visual artifacts harder to id...
arXiv:2608. 12876v1 Announce Type: cross Abstract: Detecting AI-generated images is only half the task: a deployed detector must also justify its verdict, yet existing detectors inherit three failure modes from their training data: real and fake images collected from different sources invite provenance shortcuts, supervised explanation corpora teach templated rationales, and a static forgery corpus leaves the decision boundary standing still while generators keep moving.
The paper introduces Restoring without Forgetting (RwF), a continual learning framework for image restoration that handles multiple degradations sequentially without accessing prior data. RwF trains a lightweight adapter for each new degradation, uses an unsupervised routing mechanism to select the correct restoration path, and achieves significant PSNR gains over fine‑tuning on five benchmark degradation domains. The method also demonstrates strong transfer performance on eleven real‑degradation datasets with high routing accuracy.
arXiv:2606. 26552v1 Announce Type: cross Abstract: The rapid advancement of generative models presents a significant challenge to existing deepfake detection methods, particularly given the widespread dissemination of highly realistic AI-generated images.
The paper introduces a self‑referential retrosynthesis framework for explainable AI provenance forensics that works with fixed‑generator generative models. It uses a jointly optimized encoder‑decoder pair to embed client inputs, generate high‑fidelity outputs, and then verify provenance by comparing the resynthesized image to the original query. The method eliminates the need for watermarking or generator modifications while providing interpretable evidence of a model’s output origin.
arXiv:2502. 19716v3 Announce Type: replace-cross Abstract: Recent advances in visual generative models have enabled the creation of highly realistic, fully AI-generated images without relying on real source content.
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.
The paper introduces a self-referential retrosynthesis framework for explainable AI provenance forensics that works with fixed generative models. It uses a jointly optimized encoder-decoder pair to embed client inputs, allowing the generator to produce high-fidelity outputs while enabling round-trip consistency checks. The method eliminates the need for watermarking or generator modifications and provides interpretable evidence linking generated images back to their source inputs.
The paper proposes Artifact-Complementary Expert Fusion (ACEF), a two‑stage framework that enhances AI‑generated image detection by combining two types of reconstruction artifacts—VAE/DDIM and SRGAN—into aligned synthetic negatives. ACEF first builds artifact‑specific experts using LoRA adaptation on a frozen backbone, then fuses their multi‑layer evidence with Layer‑wise Artifact‑Complementary Fusion (LACF) to mitigate conflicts between artifact manifolds. Experiments on 13 benchmarks show that this approach improves generalizability over existing state‑of‑the‑art methods.
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 challeng...