Hao-Ping (Hank) Lee (Carnegie Mellon University), Wei-Lun Kao (National Taiwan University), Hung-Jui Wang (National Taiwan University), Ruei-Che Chang (University of Michigan), Yi-Hao Peng (Carnegie Mellon University), Fu-Yin Cherng (National Chung Cheng University), Shang-Tse Chen (National Taiwan University)

Audio CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is an accessible alternative to the traditional CAPTCHA for people with visual impairments. However, the literature has found that audio CAPTCHA suffers from both lower usability and security than its visual counterpart. In this paper, we propose AdvCAPTCHA, a novel audio CAPTCHA generated by using adversarial machine learning techniques. By conducting studies with people with and without visual impairments, we show that AdvCAPTCHA can outperform the status quo audio CAPTCHA in security but not usability. We demonstrate AdvCAPTCHA’s feasibility of providing detection of malicious attacks. We also present an evaluation metric, thresholding, to quantify the trade-off between usability and security for audio CAPTCHA design. Finally, we discuss approaches to the real-world adoption of AdvCAPTCHA.

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Gradient Shaping: Enhancing Backdoor Attack Against Reverse Engineering

Rui Zhu (Indiana University Bloominton), Di Tang (Indiana University Bloomington), Siyuan Tang (Indiana University Bloomington), Zihao Wang (Indiana University Bloomington), Guanhong Tao (Purdue University), Shiqing Ma (University of Massachusetts Amherst), XiaoFeng Wang (Indiana University Bloomington), Haixu Tang (Indiana University, Bloomington)

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Don't Interrupt Me – A Large-Scale Study of On-Device...

Marian Harbach (Google), Igor Bilogrevic (Google), Enrico Bacis (Google), Serena Chen (Google), Ravjit Uppal (Google), Andy Paicu (Google), Elias Klim (Google), Meggyn Watkins (Google), Balazs Engedy (Google)

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Unus pro omnibus: Multi-Client Searchable Encryption via Access Control

Jiafan Wang (Data61, CSIRO), Sherman S. M. Chow (The Chinese University of Hong Kong)

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Improving the Robustness of Transformer-based Large Language Models with...

Lujia Shen (Zhejiang University), Yuwen Pu (Zhejiang University), Shouling Ji (Zhejiang University), Changjiang Li (Penn State), Xuhong Zhang (Zhejiang University), Chunpeng Ge (Shandong University), Ting Wang (Penn State)

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