This study seeks to discern whether images featuring avatars can be effectively used to generate adversarial examples capable of fully masking a person, and establishes a method of utilizing virtual avatars to provide increased privacy for users.
Deep Neural Networks (DNNs) remain vulnerable to adversarial perturbations, raising significant concerns in image processing applications, particularly in high-stakes domains such as medical imaging and security-critical systems. Most existing defense strategies are limited by domain specificity, architectural dependen...
Syamantak Sarkar, Nirmal Joseph, Sudhish N. George et al.· IEEE Transactions on Image P...· 0 citations
This work suggests that existing AI-generated image detectors are easily deceived by adversarial perturbations, highlighting the need for more robust detectors.
Yun-Yun Tsai, Rui Xu, Chengzhi Mao et al.· 0 citations
In this paper, we show that standard evaluations of high-resolution Model Inversion Attacks (MIAs) significantly underestimate training-data privacy leakage. State-of-the-art privacy defenses, standard training techniques such as MixUp and Adversarial Training, and undefended models all leak training images at rates 1....
Shailen Smith, Rasmus Torp, Adam Breuer· 0 citations
This work proposes two novel attack methods targeted at detectors that leverage autoencoder reconstruction error and finds that by constructing imperceptible adversarial examples, the distance between original and reconstruction can be artificially increased, causing fake images to be wrongly classified as real.
R. Demchenko, Jonas Ricker, Asja Fischer· 0 citations
Mitigating Attacks on LLMs with Privacy and Adversarial TrainingOlasunkanmi Sodunke
School of Engineering Technology, Purdue University The rapid adoption of Large Language Models (LLMs) such as BERT & GPT-3 has intensified privacy concerns, especially with vulnerabilities like model inversion and membership inference...
