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.
arXiv:2603.01433v3 Announce Type: replace Abstract: We present DOCFORGE-BENCH, the first unified zero-shot benchmark for document forgery detection, evaluating 14 methods across eight datasets spanni...
The paper evaluates whether multimodal large language models (MLLMs) can serve as forensic detectors of deepfake facial images. Six MLLMs—including GPT-4o, GPT-5, GPT-5.2, Gemini 2.5, Gemini 3, and Seed 1.6—were tested on a dataset of 400 images (50 authentic and 350 synthetic) generated by seven leading image models. Results show that several MLLMs achieve high detection accuracy, with GPT-5.2 reaching 97.9% in pairwise comparisons, but the presence of invisible SynthID watermarks can reduce detection performance by about 10% for Gemini.
arXiv:2607. 26641v1 Announce Type: cross Abstract: Identity document (ID) authentication relies on the structural integrity of complex, high-frequency security patterns.
arXiv:2604.25213v2 Announce Type: replace Abstract: OpenAI's GPT-Image-2 has effectively erased the visual boundary between authentic and AI-edited document images: a single number on a receipt can b...
arXiv:2610.33627v1 Announce Type: new Abstract: Identity-document portraits are modified during document production by printing, engraving, and integration into the document template. These transform...
arXiv:2602.20569v2 Announce Type: replace Abstract: We present AIForge-Doc, the first dedicated benchmark targeting exclusively diffusion-model-based inpainting in financial and form documents with p...
arXiv:2609.13617v1 Announce Type: new Abstract: We evaluate whether the improvements advertised for ChatGPT Images 2.5 translate into better performance on forgery tasks with predetermined answers. W...
arXiv:2609.01511v1 Announce Type: new Abstract: Face forgery detectors often achieve strong results on controlled benchmarks, but their reliability under realistic image degradations remains limited....
FARE (Forensic Acceptance Region Estimation) is a method designed to audit the integrity of AI image generators at deployment time. By training on images from a certified generator, FARE learns generator‑specific forensic features and tightens its acceptance region using hard samples, enabling it to determine whether a new image originates from the enrolled generator. Experiments show that FARE consistently outperforms existing baselines in detecting generator swaps, even when the swapped model is similar or under exact‑model and decision‑only attacks.
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.
The study evaluates 16 AI‑generated image detection methods in a zero‑shot setting, testing 23 pretrained variants on 12 datasets with 2.6 million images from 291 generators. Results show no single detector consistently outperforms others, with accuracy ranging from 37.5 % to 75.0 %. The study highlights that training data alignment and generator type significantly affect performance, especially against modern commercial models, and identifies systematic failure patterns across datasets.