Evasion Attacks Against Watermarking Techniques found in MLaaS Systems

Hitaj, Dorjan; Hitaj, Briland; Mancini, Luigi V.

doi:10.1109/sds.2019.8768572

Cited by 26 publications

(33 citation statements)

References 11 publications

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“…• Adversarial attacks (Brendel et al, 2017); • Backdoor attacks 6 (Chen et al, 2017;Gu et al, 2019); • Cyber kill chain-based attack (Nguyen, 2017); • Data manipulation attacks (Liao et al, 2018); • Evasion attacks (Hitaj et al, 2019);…”

Section: Attacks On Cloud-hosted Machine Learning Models: Thematic Anmentioning

confidence: 99%

“…These are listed below with their descriptions provided in the next section. Adversarial attacks ( Brendel et al, 2017 ); Backdoor attacks 6 ( Chen et al, 2017 ; Gu et al, 2019 ); Cyber kill chain–based attack ( Nguyen, 2017 ); Data manipulation attacks ( Liao et al, 2018 ); Evasion attacks ( Hitaj et al, 2019 ); Exploration attacks ( Sethi and Kantardzic, 2018 ); Model extraction attacks ( Correia-Silva et al, 2018 ; Kesarwani et al, 2018 ; Joshi and Tammana, 2019 ; Reith et al, 2019 ); Model inversion attacks ( Yang et al, 2019 ); Model-reuse attacks ( Ji et al, 2018 ); Trojan attacks ( Liu et al, 2018 ). …”

Section: Attacks On Cloud-hosted Machine Learning Models (Q1)mentioning

confidence: 99%

“…Threat model: Cloud ML attacks are based on different threat models, with the salient types with examples are listed below. black box attacks (no knowledge) ( Brendel et al, 2017 ; Chen et al, 2017 ; Hosseini et al, 2017 ; Correia-Silva et al, 2018 ; Sethi and Kantardzic, 2018 ; Hitaj et al, 2019 ); white box attacks (full knowledge) ( Liao et al, 2018 ; Liu et al, 2018 ; Gu et al, 2019 ; Reith et al, 2019 ); gray box attacks (partial knowledge) ( Ji et al, 2018 ; Kesarwani et al, 2018 ). …”

Section: Attacks On Cloud-hosted Machine Learning Models (Q1)mentioning

confidence: 99%

“…black box attacks (no knowledge) ( Brendel et al, 2017 ; Chen et al, 2017 ; Hosseini et al, 2017 ; Correia-Silva et al, 2018 ; Sethi and Kantardzic, 2018 ; Hitaj et al, 2019 );…”

Section: Attacks On Cloud-hosted Machine Learning Models (Q1)mentioning

confidence: 99%

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Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security

et al. 2020

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With the advances in machine learning (ML) and deep learning (DL) techniques, and the potency of cloud computing in offering services efficiently and cost-effectively, Machine Learning as a Service (MLaaS) cloud platforms have become popular. In addition, there is increasing adoption of third-party cloud services for outsourcing training of DL models, which requires substantial costly computational resources (e.g., high-performance graphics processing units (GPUs)). Such widespread usage of cloud-hosted ML/DL services opens a wide range of attack surfaces for adversaries to exploit the ML/DL system to achieve malicious goals. In this article, we conduct a systematic evaluation of literature of cloud-hosted ML/DL models along both the important dimensions—attacks and defenses—related to their security. Our systematic review identified a total of 31 related articles out of which 19 focused on attack, six focused on defense, and six focused on both attack and defense. Our evaluation reveals that there is an increasing interest from the research community on the perspective of attacking and defending different attacks on Machine Learning as a Service platforms. In addition, we identify the limitations and pitfalls of the analyzed articles and highlight open research issues that require further investigation.

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Section: Attacks On Cloud-hosted Machine Learning Models: Thematic Anmentioning

confidence: 99%

Section: Attacks On Cloud-hosted Machine Learning Models (Q1)mentioning

confidence: 99%

Section: Attacks On Cloud-hosted Machine Learning Models (Q1)mentioning

confidence: 99%

“…black box attacks (no knowledge) ( Brendel et al, 2017 ; Chen et al, 2017 ; Hosseini et al, 2017 ; Correia-Silva et al, 2018 ; Sethi and Kantardzic, 2018 ; Hitaj et al, 2019 );…”

Section: Attacks On Cloud-hosted Machine Learning Models (Q1)mentioning

confidence: 99%

See 2 more Smart Citations

Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security

et al. 2020

View full text Add to dashboard Cite

show abstract

“…• black box attacks (no knowledge) (Brendel et al, 2017;Chen et al, 2017;Hosseini et al, 2017;Correia-Silva et al, 2018;Sethi and Kantardzic, 2018;Hitaj et al, 2019); • white box attacks (full knowledge) (Liao et al, 2018;Liu et al, 2018;Gu et al, 2019;Reith et al, 2019); • gray box attacks (partial knowledge) (Ji et al, 2018;Kesarwani et al, 2018).…”

Section: Attacks On Cloud-hosted Machine Learning Models: Thematic Anmentioning

confidence: 99%