The threat of offensive ai to organizations
AI has provided us with the ability to automate tasks, extract information from vast amounts of
data, and synthesize media that is nearly indistinguishable from the real thing. However …
data, and synthesize media that is nearly indistinguishable from the real thing. However …
Adversarial attacks and defenses in explainable artificial intelligence: A survey
H Baniecki, P Biecek - Information Fusion, 2024 - Elsevier
Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging
and trusting statistical and deep learning models, as well as interpreting their predictions …
and trusting statistical and deep learning models, as well as interpreting their predictions …
Robustbench: a standardized adversarial robustness benchmark
As a research community, we are still lacking a systematic understanding of the progress on
adversarial robustness which often makes it hard to identify the most promising ideas in …
adversarial robustness which often makes it hard to identify the most promising ideas in …
Adversarial machine learning-industry perspectives
RSS Kumar, M Nyström, J Lambert… - 2020 IEEE security …, 2020 - ieeexplore.ieee.org
Based on interviews with 28 organizations, we found that industry practitioners are not
equipped with tactical and strategic tools to protect, detect and respond to attacks on their …
equipped with tactical and strategic tools to protect, detect and respond to attacks on their …
Adversarial exemples: A survey and experimental evaluation of practical attacks on machine learning for windows malware detection
Recent work has shown that adversarial Windows malware samples—referred to as
adversarial EXE mples in this article—can bypass machine learning-based detection relying …
adversarial EXE mples in this article—can bypass machine learning-based detection relying …
Poisoning attacks on algorithmic fairness
Research in adversarial machine learning has shown how the performance of machine
learning models can be seriously compromised by injecting even a small fraction of …
learning models can be seriously compromised by injecting even a small fraction of …
Cybersecurity and privacy in smart bioprinting
JC Isichei, S Khorsandroo, S Desai - Bioprinting, 2023 - Elsevier
Bioprinting is a versatile technology which is gaining rapid adoption in healthcare fields
such as tissue engineering, regenerative medicine, drug delivery, and surgical planning …
such as tissue engineering, regenerative medicine, drug delivery, and surgical planning …
Indicators of attack failure: Debugging and improving optimization of adversarial examples
Evaluating robustness of machine-learning models to adversarial examples is a challenging
problem. Many defenses have been shown to provide a false sense of robustness by …
problem. Many defenses have been shown to provide a false sense of robustness by …
Scaling compute is not all you need for adversarial robustness
The last six years have witnessed significant progress in adversarially robust deep learning.
As evidenced by the CIFAR-10 dataset category in RobustBench benchmark, the accuracy …
As evidenced by the CIFAR-10 dataset category in RobustBench benchmark, the accuracy …
Do gradient-based explanations tell anything about adversarial robustness to android malware?
While machine-learning algorithms have demonstrated a strong ability in detecting Android
malware, they can be evaded by sparse evasion attacks crafted by injecting a small set of …
malware, they can be evaded by sparse evasion attacks crafted by injecting a small set of …