Adversarial classification

By Nilesh N. Dalvi, Pedro M. Domingos, Mausam, Sumit K. Sanghai, D. Verma. 2004

1,071 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems, Safety, Robustness & Interpretability

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Citations in the corpus

Adversarial Examples for Evaluating Reading Comprehension Systems (2017, background): “Dalvi et al. (2004) formulated such tasks as a game between a classifier and an adversary, and analyzed optimal strategies for each player.”
Towards Deep Learning Models Resistant to Adversarial Attacks (2017, background): “We also want to note that the study of adversarial ML predates the widespread use of deep neural networks [8, 10] (see [3] for an overview of earlier work).”
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models (2017, background): “Other decision-based attacks are speciﬁc to linear or convex-inducing classiﬁers (Dalvi et al., 2004; Lowd & Meek, 2005; Nelson et al., 2012) and are not applicable to other machine learning models.”
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain (2017, background): “Here the attacker’s goal was to either craft messages that evade detection [25], [26], [27], [28] to let spam through or influence its training data to cause it to block legitimate messages.”
Adversarial Machine Learning at Scale (2016, background): “It has been shown that machine learning models are often vulnerable to adversarial manipulation of their input intended to cause incorrect classification (Dalvi et al., 2004).”
Stealing Machine Learning Models via Prediction APIs (2016, background): “In that context, a number of authors have considered strategies and defenses for poisoning attacks, that consist in injecting maliciously crafted samples into a model’s train or test data, so as to decrease the learned model’s accuracy [10,21,32,40,45].”
Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners (2015, background): “Introduction The study on security threats to intelligent agents has a long history (Nelson et al. 2009; Tan, Killourhy, and Maxion 2002; Rubinstein et al. 2008; Barreno et al. 2010; Laskov and Lippmann 2010; Barreno et al. 2006; Dalvi et al. 2004; Liu and Chawla 2009; Biggio, Fumera, and Roli 2013;”
Evasion Attacks against Machine Learning at Test Time (2013, background): “2 This is also the setting adopted in previous work [9, 19, 22].”
Model-Reuse Attacks on Deep Learning Systems (2018, background): “(ii) Evasion attacks, in which the adversary modifies the input data during inference to trigger the systems to misbehave [7, 18, 37, 42].”
Machine Learning Models that Remember Too Much (2017, background): “Poisoning attacks [9, 18, 38, 57, 65] insert malicious data points into the training dataset to make the resulting model easier to evade.”
Adversarial examples in the physical world (2016, background): “However, machine learning models are often vulnerable to adversarial manipulation of their input intended to cause incorrect classification (Dalvi et al., 2004).”
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning (2018, neutral): “, [2], [4], [14], [25], [34].”
On the (Statistical) Detection of Adversarial Examples (2017, methodology): “Other approaches make use of game theory [7, 19, 5].”
Data Poisoning Attacks on Factorization-Based Collaborative Filtering (2016, methodology): “In this paper we provide a novel technique based on stochastic gradient Langevin dynamics optimization [10] to produce malicious users that mimic normal user behaviors in order to avoid detection, while achieving attack objectives.”
Is Feature Selection Secure against Training Data Poisoning? (2015, background): “In several cases, these constraints can be encoded in terms of distance measures between the original, non-manipulated attack samples and the manipulated ones (Dalvi et al., 2004; Lowd & Meek, 2005; Globerson & Roweis, 2006; Teo et al., 2008; Brückner et al., 2012; Biggio et al., 2013a).”
The security of machine learning (2010, background): “This category of attack has been studied in detail in the literature (Lowd and Meek 2005a, 2005b; Wittel and Wu 2004; Dalvi et al. 2004).”
Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers (2016, background): “The risk of evasion attacks against machine learning models under adversarial settings has been discussed in the machine learning community, mainly focused on simple models for spam detection (e.g., [10, 18]).”
Evasion and Hardening of Tree Ensemble Classifiers (2015, background): “While there is a prolific literature considering the evasion of linear or otherwise differentiable models (Dalvi et al., 2004; L., 2005; Lowd & Meek, 2005; Nelson et al., 2012; Brückner et al., 2012; Fawzi et al., 2014; Biggio et al., 2013; Szegedy et al., 2013; Srndic & Laskov, 2014), we are only a”