13 papers
Open-Jev Judgments on CallScreenBench: Calibrated One-Pass Scam Screening with a Small Language Model
Simiao Ren, Kidus Zewde, Xingyu Shen +6
Screening a phone call for fraud needs a trustworthy probability after every caller turn, in milliseconds. Jev-style typed decisions promise exactly that: declared options go in, o…
Agents That Edit Documents: Measuring Agentic PDF Forgery Against a Non-Agentic Control
Simiao Ren, Ankit Raj, Tommy Duong +6
AI agents that carry a multi-step computer task through on their own became ordinary tools in the past year, and the same autonomy is available to anyone whose task is harmful. We…
ChatGPT Images 2.5 in the Wild: A Launch-Period Dataset and Detector Evaluation
Dennis Ng, Xingyu Shen, Ankit Raj +6
An image tool can change its underlying generator while retaining its public name, making version attribution from online posts ambiguous. We study this problem after the ChatGPT I…
ChatGPT Images 2.5 on Forgery Tasks: Testing Advertised Improvements Against Known Answers
Ankit Raj, Yuxin Zhang, Kidus Zewde +7
OpenAI released ChatGPT Images 2.5 on 8 September 2026, advertising more precise local edits, better consistency across edits, more faithful reference products and sharper detail.…
Anatomy of a Scam Call: What 10,000 real scam and spam calls reveal about how phone scammers operate
Ethan Traister, Ankit Raj, Jiaqi Gan +7
Telephone fraud is pervasive and costly, but its inner workings are rarely observed at scale. We analyze a complete corpus of 10,211 inbound scam and spam calls -- 913 hours of aud…
CallScreenBench: Benchmarking Small Language Models as Phone Secretaries
Jiaqi Gan, Haoyuan Tang, Jamey Z. Liang +6
Language models small enough to run on a handset, quantized to a few bits, are increasingly capable of acting on their user's behalf -- which makes on-device task automation newly…
GPT-Image-2 in the Wild: A Twitter Dataset of Self-Reported AI-Generated Images from the First Week of Deployment
Kidus Zewde, Simiao Ren, Xingyu Shen +6
The release of GPT-image-2 by OpenAI marks a watershed moment in AI-generated imagery: the boundary between photographic reality and synthetic content has never been more difficult…
When the Forger Is the Judge: GPT-Image-2 Cannot Recognize Its Own Faked Documents
Jiaqi Wu, Yuchen Zhou, Dennis Tsang Ng +5
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 be replaced in under a second for…
A Synthetic Eye Movement Dataset for Script Reading Detection: Real Trajectory Replay on a 3D Simulator
Kidus Zewde, Yuchen Zhou, Dennis Ng +6
Large vision-language models have achieved remarkable capabilities by training on massive internet-scale data, yet a fundamental asymmetry persists: while LLMs can leverage self-su…
How well are open sourced AI-generated image detection models out-of-the-box: A comprehensive benchmark study
Simiao Ren, Yuchen Zhou, Xingyu Shen +9
As AI-generated images proliferate across digital platforms, reliable detection methods have become critical for combating misinformation and maintaining content authenticity. Whil…
Can Multi-modal (reasoning) LLMs detect document manipulation?
Zisheng Liang, Kidus Zewde, Rudra Pratap Singh +7
Document fraud poses a significant threat to industries reliant on secure and verifiable documentation, necessitating robust detection mechanisms. This study investigates the effic…
Can Multi-modal (reasoning) LLMs work as deepfake detectors?
Simiao Ren, Yao Yao, Kidus Zewde +8
Deepfake detection remains a critical challenge in the era of advanced generative models, particularly as synthetic media becomes more sophisticated. In this study, we explore the…
Do Deepfake Detectors Work in Reality?
Simiao Ren, Hengwei Xu, Tsang Ng +7
Deepfakes, particularly those involving faceswap-based manipulations, have sparked significant societal concern due to their increasing realism and potential for misuse. Despite ra…