Aug 2026· Journal of Computer Virology and Hacking Techniques· Vol 22· 0 citations· 59 references
TL;DR
Experimental evaluations on benchmark forensic datasets demonstrate that the proposed framework achieves competitive detection performance and robustness across most datasets, attaining an AUC of up to 0.998 on NIST16, while highlighting specific failure modes on challenging cases such as COVERAGE.
This work extends an existing detector by introducing a unified multiclass framework for image authenticity, which incorporates a segmentation branch to enable pixel-level localization of tampered regions and outperforms selected recent benchmarks.
Annalisa Gallina, M. Fiorucci, Marco Brigo et al.· 0 citations
The proposed framework establishes a multi-layered security pipeline that implements a high-precision biometric mapping and alignment phase utilizing Multi-task Cascaded Convolutional Networks to isolate facial regions and eliminate environmental noise, and establishes a statistically robust tri-state classification st...
T. Manimala, P. Sravani, V. Rajitha et al.· EPRA international journal o...· 0 citations
A multi-branch model is proposed that integratestwo pre-trained Vision Transformer models to extract complementary global features, along with a forensic and a hand-crafted feature branch, which extract low-level discriminating cues that contribute in improving the robustness and performance of the model.
Venkata Satya Renuka Devi Bhamidipati, Srinivasa Rao Chanamallu, Sudheer Gopinathan· International Journal of Ima...· 0 citations
Increasing availability of advanced AI-based image creation and editing tools has significantly increased the prevalence of digital image forgery, creating challenges for media authenticity, digital forensics, and information security. This paper presents an intelligent image forgery detection framework that combines m...
Ahmad AlMunayyer, Abdallah Banat, Mohammad AlHayajneh et al.· IEEE Jordan Conference on Ap...· 0 citations
This paper proposes a unified framework that incorporates multi-dimensional feature extraction, multi-scale feature fusion, and a confidence-guided refine mechanism that achieves state-of-the-art performance and shows strong generalization, validating its effectiveness and practicality.
Xiaolong Cheng· Poster Volume 0007 The 2026...· 0 citations
Overall, FAD-Net provides a task-oriented integration of frequency-aware feature fusion and dynamic feature refinement for image tampering localization, and achieves competitive F1 and IoU performance across the evaluated datasets, including post-processed scenarios.
Tongwei Lu, Z. Mao· Machine Vision and Applicati...· 0 citations
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