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Found 4 papers, 2 papers with code
Model-agnostic Origin Attribution of Generated Images with Few-shot Examples
In this work, we study the origin attribution of generated images in a practical setting where only a few images generated by a source model are available and the source model cannot be accessed.
An Image Is Worth 1000 Lies: Adversarial Transferability across Prompts on Vision-Language Models
Given that VLMs rely on prompts to adapt to different tasks, an intriguing question emerges: Can a single adversarial image mislead all predictions of VLMs when a thousand different prompts are given?
Perception, performance, and detectability of conversational artificial intelligence across 32 university courses
Moreover, current AI-text classifiers cannot reliably detect ChatGPT's use in school work, due to their propensity to classify human-written answers as AI-generated, as well as the ease with which AI-generated text can be edited to evade detection.
OpenFE: Automated Feature Generation with Expert-level Performance
In the two competitions, features generated by OpenFE with a simple baseline model can beat 99. 3% and 99. 6% data science teams respectively.
