arXiv Machine Learning July 9, 2026 By Sujith K Mandala

Format-Controlled Multi-Scale JPEG Compression Response Analysis for Image-Level Forgery Screening

Read the original on arXiv Machine Learning →

arXiv:2607. 06615v1 Announce Type: cross Abstract: Image forgery detection is a critical task in digital forensics, yet many deep-learning localization approaches are typically GPU-accelerated and computationally heavier than handcrafted screening methods.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Field-Localized Forgery Detection for Digital Identity Documents

arXiv:2605. 09089v2 Announce Type: replace-cross Abstract: Digital onboarding and eKYC systems used by banks, fintech platforms, telecom providers, and other third-party services commonly verify users by comparing an uploaded identity document with a selfie or live facial capture.

By Abhishek Kumar, Riya Tapwal, Carsten Maple, Mark Hooper

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Jul 16

Continuously Evolving Deepfake Detection: An Architecture and Public-Benchmark Evaluation of a Dynamic Detection System

arXiv:2607. 13234v1 Announce Type: cross Abstract: Deepfake detectors that achieve near-perfect scores on academic benchmarks collapse on real-world content: recent in-the-wild evaluations report AUC drops of 45-50% for state-of-the-art open-source models.

By Ken Jon Miyachi, Dylan Uys

robotics benchmarks safety

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Jul 28

Hybrid Semantic and Spectral Ensemble for Robust Synthetic Image Source Attribution

arXiv:2607. 22808v1 Announce Type: cross Abstract: The rapid advancement of text-to-image (T2I) models has necessitated robust Synthetic Image Source Attribution (SIA) methodologies.

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Hugging Face Trending Papers

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Beyond Visual Forensics: Auditing Multimodal Robustness for Synthetic Medical Image Detection

With the rapid adoption of generative AI, synthetic medical images pose growing risks, including diagnostic deception and insurance fraud. Although prior work has explored vision-language model (VLM)-based synthetic image detection, these evaluations typically consider images in isolation.

llms multimodal benchmarks

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Jul 2

Text Over Image: Auditing Multimodal Robustness in Synthetic Medical Image Detection

arXiv:2606. 25375v2 Announce Type: replace-cross Abstract: With the rapid adoption of generative AI, synthetic medical images pose growing risks, including diagnostic deception and insurance fraud.

By Ching-Hao Chiu, Hao-Wei Chung, Gelei Xu, Xueyang Li, Pin-Yu Chen, John Kheir, Meysam Ghaffari, Carlos Morato, Ahmed Abbasi, Yiyu Shi

llms multimodal benchmarks

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Jul 8

VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection

arXiv:2607. 06254v1 Announce Type: cross Abstract: Deepfake image detection is currently served by three fundamentally different paradigms: commercial APIs, zero-shot vision-language models (LLMs), and open-source detectors.

By Sharayu N. Deshmukh, Md Rashidunnabi, Nelton Tiago Gemo, Kurundkar G. D., Mahamune M. R., Nilesh K. Deshmukh

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Field-Localized Forgery Detection for Digital Identity Documents

Continuously Evolving Deepfake Detection: An Architecture and Public-Benchmark Evaluation of a Dynamic Detection System

Hybrid Semantic and Spectral Ensemble for Robust Synthetic Image Source Attribution

Beyond Visual Forensics: Auditing Multimodal Robustness for Synthetic Medical Image Detection

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