DBINDS: detection based on initial noise difference sequence from diffusion model inversion for AI-generated videos

Structured Local Differential Modeling for AI-Generated Image Detection

RippleNet is proposed, an AI-generated image detection framework based on local differential signals that adaptively identifies forgery-sensitive regions and constructs multi-directional, multi-scale differential representations within local neighborhoods, explicitly characterizing anomalous patterns in neighborhood statistics.

Understanding Why Foundation Models Work for Diffusion-Generated Image Detection

This work investigates what cues are exploited by foundation-model-based detectors to distinguish real images from diffusion-generated ones and suggests that foundation-model-based detectors succeed by capturing non-semantic low-to-mid frequency distributional discrepancies between real and diffusion-generated images.

FUSED: Forensic-Semantic Mixture-of-Experts for AI Inpainting Detection and Localization

Diffusion-based inpainting models modify only a localized part of an image, while many AI-image detectors rely on global artifacts and do not localize. These artifacts vary across generators, limiting detector transfer under distribution shifts. Recent work shows that restoring the authentic pixels outside the inpainted region removes these cues and can degrade pretrained detectors. To address this, we present FUSED, a unified framework for the joint detection and localization of AI-generated inpainting. FUSED combines low-level forensic cues with high-level semantic features using a sparsely-gated Mixture-of-Experts architecture, enabling the model to adaptively prioritize the most relevant signal for each token. For each input, FUSED predicts both an image-level manipulation score and a pixel-level mask of the inpainted area. On the OpenSDID cross-generator benchmark, FUSED achieves the best average detection and localization, with the largest gains on unseen generators. The same model transfers directly to the held-out AutoSplice and CocoGlide benchmarks, more than doubling localization performance. Evaluating each held-out benchmark with and without the global generator artifact further shows that all evaluated methods, ours included, partly read the artifact as evidence of manipulation, and FUSED remains the strongest under both conditions. Code and pretrained models are available at https://github.com/AntonNuzhdin/FUSED.

Comparative Analysis of Frequency-Domain and Spatial-Domain Deep Learning Methods for Detecting Diffusion-Generated Human Faces

DTBF: Combining Local Statistical Artifacts and Concept Alignment for Synthetic Image Detection

In general, the cross-generator generalization and robustness against attacks are two main challenges in AI-generated image detection. To address this, we put forward a synthetic image detector (DTBF), a two-branch architecture followed by a jointly-optimized concatenation (JOC), aiming at capturing low-level artifacts and high-level semantics and dynamically fusing them to enhance the generalization and robustness. In the artifact-extracted branch (AE-branch), the local multi-direction binary-encoding patterns (LMBP) are customized to extract and encode the relationships among pixels within each sliding window, resulting in the LMBP distribution serving as a universal fingerprint to distinguish real/fake images. The dual-alignment guided semantic branch (DAS-branch), working as a supplement to AE-branch, designs global context-unrelated prompts and semantic-enhanced prompts to capture global semantic inconsistency and local patch anomalies, strengthening the generalization and robustness of DTBF. Finally, JOC dynamically fuses the two branches through concatenation to amplify the effectiveness of each branch, achieving better generalization and robustness. With the assistance of two complementary branches and JOC, our proposed DTBF significantly outperforms 12 state-of-the-art detectors on two publicly available datasets in terms of detection accuracy and robustness.

GPE-YOLO: a gradient-prior enhanced detector with dynamic sampling for robust object detection in adverse weather

GPE-YOLO is proposed, a robust detection framework built upon the YOLOv11 architecture that explicitly integrates multiscale edge priors to enhance feature resilience and validate the potential of GPE-YOLO for reliable deployment in real-world adverse weather scenarios.