A specialized dual-stream framework that strategically integrates features from both spatial and frequency domains to disentangle the fingerprints of heterogeneous generators is proposed, addressing two critical tasks: binary real/fake detection and closed-set source attribution across 10 distinct generative architectures.
This work introduces a dual-branch ensemble framework fusing Semantic Deep Learning with Mathematical Forensic Feature Extraction, highlighting the practicality and scalability of mathematical forensics for real-world deployment.
This work proposes GenRes, a novel framework for generative residual learning via a neural tensor network, which models fine-grained relational features between original and transformed samples to enhance generalization in scenarios involving multiple generative transformations.
The proliferation of hyper-realistic AI-generated images poses significant threats to digital information integrity and forensic accountability. Existing detection methodologies, however, face three critical bottlenecks: vulnerability to real-world distortions such as social media compression, inadequate specialization for socially harmful “ex-regulatory” content, and an inability to perform model attribution essential for effective governance. To address these challenges, we propose the Multi-level Feature Fusion Detection and Attribution Framework (MF2DA), a unified end-to-end pipeline designed for both high-precision detection and reliable model attribution. The core architecture synergizes an Edge Pyramid Fusion ResNet (EPF-ResNet), which captures subtle pixel-level edge artifacts, with a frozen CLIP-ViT to ensure robust semantic generalization. Furthermore, the framework is augmented by the MLLM-Guided Quality Refinement Module (MQRM), which adaptively leverages semantic-agnostic quality features to decouple generative traces from aggressive compression noise. Finally, the Dual-stream Differential Patch Attribution Network (D2PAN) extracts resilient model fingerprints by disentangling micro-textural patterns from semantic interference, thereby achieving precise generator identification. Extensive evaluations on multiple benchmarks demonstrate that MF2DA achieves state-of-the-art performance in detecting both “friendly” and “ex-regulatory” images while maintaining exceptional cross-dataset generalization. By integrating robust detection with precise attribution, this work establishes a practical and accountable forensic solution for the rapidly evolving generative AI landscape.
Wenpeng Mu, Qiang Xu, Yi-Ning Zhang et al.· IEEE Transactions on Informa...· 0 citations
Overall, GenPix provides a challenging and realistic benchmark for evaluating modern detectors, and the proposed AAE offers an efficient, interpretable baseline for future research on general-purpose fake-image detection.
Guessoum Dalila, B. Nadjia, Boumahdi Fatima et al.· Iraqi Journal for Computer S...· 0 citations
AI-generated videos are becoming increasingly realistic and difficult to distinguish from authentic ones, which facilitates malicious misuse and poses growing threats to cybersecurity and social governance. Attributing AI-generated videos to their specific generative sources is therefore of critical importance for forensic investigation and legal regulation. However, most existing visual attribution methods focus on images and particularly rely on the image generation model, thereby lacking the ability to generalize to large-scale AI-generated video data. To address these limitations, we introduce an training-free AI-generated video attribution paradigm. Specifically, we formulates AI-generated video attribution as an instance retrieval task, and design a generative fingerprint-based pipeline. This pipeline consists of an adapted orthogonal color transformation, multi-scale quantized residual generation, and temporal-semantic aggregation, progressively capturing and integrating artifacts introduced by generative models across video frames. Extensive experiments on the GenVidBench benchmark demonstrate that our method achieves strong performance in both AI-generated video detection and attribution, outperforming existing state-of-the-art methods with a Rank-1 accuracy of 20.5% and a mean Average Precision of 16.6%. The code is at https://github.com/renxi-seu/Video_Attribution.
R. Cheng, Chaolei Han, Jie Gui et al.· arXiv.org· 0 citations
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.
Nadia Ali· Al-Noor Journal of Engineeri...· 0 citations
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