CleaNN

Javaheripi, Mojan; Samragh, Mohammad; Fields, Gregory; Javidi, Tara; Koushanfar, Farinaz

doi:10.1145/3400302.3415671

Cited by 20 publications

(1 citation statement)

References 22 publications

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“…Finally, some methods explore stacking multiple binary tests to create more robust defenses. For instance, the CleaNN defense [157] uses two anomaly detectors: one at the input level using frequency analysis to detect suspicious patterns in an image; the other at one of the suspicious DNN's intermediary layers so as to capture abnormal features associated with the image's representations. Any of the two detectors being triggered leads to the defense rejecting the input.…”

Section: ) Data-based Backdoor Defensesmentioning

confidence: 99%

A Comprehensive Survey on Backdoor Attacks and Their Defenses in Face Recognition Systems

Roux,

Bourbao,

Teglia

et al. 2024

IEEE Access

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Deep learning has significantly transformed face recognition, enabling the deployment of large-scale, state-of-the-art solutions worldwide. However, the widespread adoption of deep neural networks (DNNs) and the rise of Machine Learning as a Service emphasize the need for secure DNNs. This paper revisits the face recognition threat model in the context of DNN ubiquity and the common practice of outsourcing their training and hosting to third-parties. Here, we identify backdoor attacks as a significant threat to modern DNN-based face recognition systems (FRS). Backdoor attacks involve an attacker manipulating a DNN's training or deployment, injecting it with a stealthy and malicious behavior. Once the DNN has entered its inference stage, the attacker may activate the backdoor and compromise the DNN's intended functionality. Given the critical nature of this threat to DNN-based FRS, our paper comprehensively surveys the literature of backdoor attacks and defenses previously demonstrated on FRS DNNs. As a last point, we highlight potential vulnerabilities and unexplored areas in FRS security.

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Section: ) Data-based Backdoor Defensesmentioning

confidence: 99%

A Comprehensive Survey on Backdoor Attacks and Their Defenses in Face Recognition Systems

Roux,

Bourbao,

Teglia

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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SGBA: A stealthy scapegoat backdoor attack against deep neural networks

He,

Shen,

Xia

et al. 2024

Computers & Security

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Hardware-Assisted Machine Learning in Resource-Constrained IoT Environments for Security: Review and Future Prospective

Kornaros¹

2022

IEEE Access

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As the Internet of Things (IoT) technology advances, billions of multidisciplinary smart devices act in concert, rarely requiring human intervention, posing significant challenges in supporting trusted computing and user privacy, as well as protecting against attacks such as spoofing, denial of service (DoS), jamming, and eavesdropping. To tackle attacks on the IoT and cyber-physical ecosystem, many intrusion detection and security approaches have been presented in the literature. Machine learning (ML) based intrusion and anomaly detection has lately gained traction due to its capacity to cope with encrypted and rapidly developing threat techniques. This work investigates into machine learning (ML) and deep learning (DL) methodologies for IoT device security and examine the benefits, drawbacks, and potential. To protect an IoT infrastructure, various solutions look into hardware-based methods for ML-based IoT authentication, access control, secure offloading, and malware detection schemes. This review aims to illuminate the value of various approaches for addressing IoT security in a truly effective, flexible, and seamless manner, as well as to provide answers to questions about tradeoffs in integrating accelerators and customizing embedded device architectures for effective use of ML-based methods. INDEX TERMSAI-based IoT security, Hardware-based machine learning, IoT intrusion detection, Trusted embedded devices I. INTRODUCTION

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CleaNN

Cited by 20 publications

References 22 publications

A Comprehensive Survey on Backdoor Attacks and Their Defenses in Face Recognition Systems

A Comprehensive Survey on Backdoor Attacks and Their Defenses in Face Recognition Systems

SGBA: A stealthy scapegoat backdoor attack against deep neural networks

Hardware-Assisted Machine Learning in Resource-Constrained IoT Environments for Security: Review and Future Prospective

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