Poisoning Adaptive Biometric Systems

By Battista Biggio, Giorgio Fumera, Fabio Roli, Luca Didaci. 2012

56 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Safety, Robustness & Interpretability, Privacy & Security, Signal Processing

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In Information Systems

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In Signal Processing

Citations in the corpus

Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning (2017, background): “Biggio et al. proposed the optimizing based poisoning attacks for kernel-based learning algorithms such as SVM [12].”
Model-Reuse Attacks on Deep Learning Systems (2018, background): “For example, autonomous vehicles can be misled to crashing [59]; video surveillance can be maneuvered to miss illegal activities [17]; phishing pages can bypass web content filtering [35]; and biometric authentication can be manipulated to allow improper access [8].”
Security Evaluation of Support Vector Machines in Adversarial Environments (2014, background): “They include specific application examples in different areas, such as intrusion detection in computer networks [7, 39, 40, 58], spam filtering [7, 52], and, most recently, even biometric authentication [9, 13].”
Support vector machines under adversarial label contamination (2015, methodology): “We formalize a corresponding optimal attack strategy, and solve it by means of heuristic approaches to keep the computational complexity tractable.”
Pattern Recognition Systems under Attack: Design Issues and Research Challenges (2014, background): “Users are authenticated by computing a similarity score s(F, T ) between the submitted face image F and the stored template T of the claimed identity: if s(F, T ) > ta (a pre-defined acceptance threshold), the claimed identity is authenticated as genuine, otherwise it is rejected as an impostor…”
A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View (2018, background): “, adaptive facial recognition systems (FRSs) [9], [38]–[40], malware classification [41], spam detection [6], etc.”
Towards Adversarial Malware Detection (2018, background): “er. If the attack aims to indiscriminately increase the classification error at test time, causing a denial of service to legitimate system users, it is referred to as a poisoning availability attack [6, 7, 10, 14, 16, 44, 45, 65, 68]. Conversely, if the attack is targeted to only have few samples m”
Pattern Recognition Systems under Attack (2013, methodology): “In [7,4] we have derived optimal poisoning attacks against adaptive face verification systems, i.”
Cracking Classifiers for Evasion: A Case Study on the Google's Phishing Pages Filter (2016, methodology): “This kind of attack has been used to degrade the performance of a lot of learning-based applications, such as biometric authentication [17][19], spam filtering [44], and network intrusion detection [34][49].”
Poisoning attacks to compromise face templates (2013, methodology): “In this section we briefly summarize the work in [1], where the authors considered a PCA-based face verification system with one template per client.”