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

A visual-neural network for specific objects-of-interest inpainting

LaP-Forensics: Latent-Pixel Consistency Guided Multimodal Reasoning for Deepfake Detection

LaP-Forensics is presented, a multimodal framework that augments RGB semantics with reconstruction-based forensic evidence that supports the utility of the residual stream under the evaluated settings, while free-form textual faithfulness and reliability under post-processing remain open limitations.

Attention-Based Deep Learning Pipeline for AI-Created Image Recognition

The proposed Attention-Based Deep Learning Pipeline of AI-Created Image Recognition incorporates three integrated branches, including low-level statistical feature extraction, high-level semantic representation learning, and attention-based feature refinement mechanism, which support the robustness and generalization ability of the proposed model in detecting AI-generated images in a variety of generators and conditions.

From Detection to Localization: A Unified Forensics Framework for Fully Synthetic and Tampered Images

The rapid advancement of generative models has significantly worsened the problem of manipulated image detection, as these methods are capable of producing highly realistic forgeries, reinforcing the importance of multimedia forensics. Conventional approaches typically frame image manipulation detection as a binary classification task (real vs. generated), which limits the capability to distinguish and localize different forms of manipulation. To address these constraints, this work extends an existing detector by introducing a unified multiclass framework (real vs. fully generated vs. tampered). In addition to classifying image authenticity, the framework incorporates a segmentation branch to enable pixel-level localization of tampered regions. The proposed approach outperforms selected recent benchmarks, offering an efficient solution with improved classification accuracy and higher IoU scores for the localization task. Find the code at https://github.com/anngal01/From-Detection-to-Localization-A-Unified-Forensics-Framework-for-Fully-Synthetic-and-Tampered-Images.

Wavelet-based features to improve cross-forgery generalization in deepfake detection

This work explores an approach that integrates wavelet-based frequency analysis with deep learning to enhance deepfake detection, and suggests that wavelet sub-bands expose manipulation cues that are useful for detecting unseen fake classes, but they should not be interpreted as a uniform robustness improvement.

Evidence Fusion for Analyzing Multimodal Image Manipulation

In this paper, we propose a unified multimodal forensic framework for joint image-authenticity detection and manipulation localization. The proposed method integrates frequency-domain features cues with visual representations to improve manipulation-sensitive feature learning. In addition, an object-context mask-refinement module is introduced to enhance pixel-level localization through contextual attention and boundary-aware residual refinement. We compare the proposed method against ten existing state-of-the-art approaches on benchmark datasets for image-level detection and pixel-level localization. Experimental results show that the proposed method achieves the best image-level detection performance, with an overall classification accuracy and Macro F1-score of 99.1%, demonstrating robust discrimination among real, fully synthetic, and tampered images. Furthermore, it achieves category-specific accuracies of 98.8%, 99.9%, and 98.5% for real, fully synthetic, and tampered images, respectively, highlighting its effectiveness across diverse generation and manipulation scenarios.