Perceptual Hash Inversion Attacks on Image-Based Sexual Abuse Removal Tools

被引：0
|
作者：
Hawkes, Sophie [1 ]
Weinert, Christian [2 ]
Almeida, Teresa [3 ,4 ]
Mehrnezhad, Maryam [2 ]
机构：
[1] Royal Holloway Univ London, Ctr Doctoral Training Cyber Secur, Egham TW20 0EX, England
[2] Royal Holloway Univ London, Informat Secur Grp, Egham TW20 0EX, England
[3] Umea Univ, Dept Informat, SE-90187 Umea, Sweden
[4] Interact Technol Inst LARSyS, P-1049001 Lisbon, Portugal
来源：
IEEE SECURITY & PRIVACY | 2024年
关键词：
Web sites; Social networking (online); Privacy; Image reconstruction; Databases; Videos; Safety; Robustness; Fingerprint recognition; Encryption;
D O I：
10.1109/MSEC.2024.3485497
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We show that perceptual hashing, crucial for detecting and removing image-based sexual abuse (IBSA) online, faces vulnerabilities from low-budget inversion attacks based on generative AI. This jeopardizes the privacy of users, especially vulnerable groups. We advocate to implement secure hash matching in IBSA removal tools to mitigate potentially fatal consequences.
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页数：11
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