Secure Kernel Machines against Evasion Attacks

By Paolo Russu, Ambra Demontis, Battista Biggio, Giorgio Fumera, Fabio Roli. 2016

80 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Safety, Robustness & Interpretability, Statistical Learning Theory, Signal Processing

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Citations in the corpus

Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning (2017, methodology): “10 [71, 72].”
Adversarial Detection of Flash Malware: Limitations and Open Issues (2017, background): “], [9], [13], [15], [17], [26].”
Is Deep Learning Safe for Robot Vision? Adversarial Examples Against the iCub Humanoid (2017, methodology): “, like in the case of decision trees), one may use a surrogate differentiable classifier to approximate it, as also suggested in [4, 8, 22].”
Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection (2017, methodology): “It is finally worth remarking that we have also recently exploited the proposed learning algorithm to improve the security of PDF and Javascript malware detection systems against sparse evasion attacks [20], [38].”
Enhancing robustness of machine learning systems via data transformations (2017, background): “However, very few defenses [39], [22] exist against these attacks, and the applicability of each is limited to only certain known attacks and speciﬁc types of ML classiﬁers (see Section VII for a detailed description of and comparison with previous work).”
Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks (2018, methodology): “For images, the former constraint is used to implement either dense or sparse evasion attacks [12,25,37].”
Adversarial Examples on Object Recognition (2020, background): “Practical robust counterparts for linear models or Support Vector Machines (SVMs) [156] rely on adding penalty terms to the loss function and have been used to protect against adversarial examples [37, 144].”
Feature Conservation in Adversarial Classifier Evasion: A Case Study (2017, background): “These range from leveraging traditional machine learning techniques, such as regularization [35], [36], [23], [28], to game theoretic approaches in which the defender chooses a classiﬁer that the attacker attempts to evade [7], [4], [18], [39], [5], to retraining with adversarial evasion examples…”
Explaining Black-box Android Malware Detection (2018, methodology): “The idea is to train a differentiable approximation f̂(x) of the target function f(x), similar to what has been done in [3] for interpretability of non-differentiable models, and in [4], [15] to craft gradient-based evasion attacks against non-differentiable learning algorithms.”
Attacks on Machine Learning: Lurking Danger for Accountability (2019, background): “Attack [4] Evasion Attack [3, 24, 30, 61, 66] Feature Deletion [28] Adversarial Examples [31, 52, 66, 71]”
Hardening Classifiers against Evasion: the Good, the Bad, and the Ugly (2017, background): “Instead, these consider the interaction as a non-zero-sum game, either played simultaneously between the learner and the attacker, or a Stackelberg game, in which the learner is the leader, while the attacker the follower [4], [18], [16], [27].”