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AI in Dermato-Oncology: Diagnostic Performance and Prompt-Injection Vulnerability of Vision-Language Models in Dermoscopic Skin Cancer Assessment. [PDF]
Cancers (Basel)Güler I +5 more
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AB jailbreaking - a novel hybrid framework for exploitation of adversarial vulnerabilities in LLMs. [PDF]
Sci RepAhmad A +3 more
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Ensuring the integrity of AI models: a blockchain-based approach for protecting medical imaging training data. [PDF]
Sci RepShinde R, Patil S, Kotecha K, Mishra S.
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Learning Universal Adversarial Perturbation by Adversarial Example
Proceedings of the AAAI Conference on Artificial Intelligence, 2022Deep learning models have shown to be susceptible to universal adversarial perturbation (UAP), which has aroused wide concerns in the community. Compared with the conventional adversarial attacks that generate adversarial samples at the instance level, UAP can fool the target model for different instances with only a single perturbation, enabling us to
Maosen Li +4 more
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On The Generation of Unrestricted Adversarial Examples
2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), 2020Adversarial examples are inputs designed by an adversary with the goal of fooling the machine learning models. Most of the research about adversarial examples have focused on perturbing the natural inputs with the assumption that the true label remains unchanged.
Mehrgan Khoshpasand +1 more
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On the Salience of Adversarial Examples
2019Adversarial examples are beginning to evolve as rapidly as the deep learning models they are designed to attack. These intentionally-manipulated inputs attempt to mislead the targeted model while maintaining the appearance of innocuous input data. Countermeasures against these attacks that take a global approach tend to be lossy to the original data ...
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Adversarial Examples for Malware Detection
2017Machine learning models are known to lack robustness against inputs crafted by an adversary. Such adversarial examples can, for instance, be derived from regular inputs by introducing minor—yet carefully selected—perturbations.
Kathrin Grosse +4 more
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Advops: Decoupling Adversarial Examples
Pattern Recognition, 2023Donghua Wang 0001 +3 more
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