AI-enabled phishing, deepfakes, and manipulation
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Search insteadMilka Kumala Kumala, Tuti Handayani, Muhammad Rizki +1
The rapid development of Artificial Intelligence (AI) has introduced deepfake technology, which is capable of manipulating audiovisual data with high realism. While this technology offers creative utility, its misuse for synthesizing non-consensual pornography, political…
Jerson Francia, Derek Hansen, Benjamin Schooley +1
Large language models can insert workplace details into phishing pretexts at low cost, but those details may either support or undermine a message's credibility. We recruited 180 U.S. working adults to evaluate simulated, AI-generated phishing emails in a disclosed survey. The…
Justin D. Norman, Sarah Barrington
The proliferation of AI-generated audio (so-called "deepfake" audio) poses significant threats to information integrity, from voice cloning fraud to synthetic music copyright disputes. We present a temporal coherence analysis framework built upon Contrastive Language-Audio…
Rafael M. Mamede, Pedro C. Neto, Ana F. Sequeira
Deepfake detectors remain vulnerable to transfer-based black-box attacks, in which adversarial examples are generated on a source surrogate model and transferred to a target model, unknown to the attacker. Yet how source--target compatibility shapes attack success remains poorly…
Guy Frankovits, Lior Yasur, Fred M. Grabovski +1
This paper presents DF-CAPTCHA, an active defense against real-time deepfake impersonation in voice and video calls. Instead of passively searching for artifacts, DF-CAPTCHA prompts the caller to perform simple challenge-response tasks that are easy for humans but difficult for…
Mengzhe Geng, Yujia Lu, Patrick Littell +2
Speech deepfakes can mimic a speaker's voice convincingly enough to deceive listeners and automated systems. This has driven strong progress in speech deepfake detection, but most detectors still end with one score per utterance. That score is useful for ranking systems, yet it…
Richard Fang, Rohan Bindu, Akul Gupta +2
Demonstrates that LLM agents can autonomously perform web hacking tasks including SQL injection, XSS, and CSRF attacks without human guidance.
Yi Zeng, Hongpeng Lin, Jingwen Zhang +3
Applies social science persuasion techniques to jailbreak LLMs, showing high attack success rates using persuasion taxonomy.