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Found 3 papers, 1 papers with code
The Adversarial AI-Art: Understanding, Generation, Detection, and Benchmarking
In a benchmarking study, we further evaluate if state-of-the-art open-source and commercial AI image detectors can effectively identify the images in the ARIA dataset.
On the Detectability of ChatGPT Content: Benchmarking, Methodology, and Evaluation through the Lens of Academic Writing
In this paper, we aim to present a comprehensive study of the detectability of ChatGPT-generated content within the academic literature, particularly focusing on the abstracts of scientific papers, to offer holistic support for the future development of LLM applications and policies in academia.
Hide and Seek: on the Stealthiness of Attacks against Deep Learning Systems
In this paper, we present the first large-scale study on the stealthiness of adversarial samples used in the attacks against deep learning.
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