Leveraging Extracted Model Adversaries for Improved Black Box Attacks
We present a method for adversarial input generation against black box models for reading comprehension based question answering. Our approach is composed of two steps. First, we approximate a victim black box model via model extraction (Krishna et al., 2020). Second, we use our own white box method to generate input perturbations that cause the approximate model to fail. These perturbed inputs are used against the victim. In experiments we find that our method improves on the efficacy of the AddAny---a white box attack---performed on the approximate model by 25% F1, and the AddSent attack---a black box attack---by 11% F1 (Jia and Liang, 2017).
Code (0)
등록된 구현이 없습니다.
Tasks
Model extractionQuestion AnsweringReading ComprehensionSimilar Papers 제목 키워드 기반
Class-feature Watermark: A Resilient Black-box Watermark Against Model Extraction Attacks
Machine learning models constitute valuable intellectual property, yet remain vulnerable to model extraction attacks (MEA), where adversaries replicate their functionality through black-box queries. Model watermarking co…
Model extractionBoosting Physical Layer Black-Box Attacks with Semantic Adversaries in Semantic Communications
End-to-end semantic communication (ESC) system is able to improve communication efficiency by only transmitting the semantics of the input rather than raw bits. Although promising, ESC has also been shown susceptible to …
Semantic CommunicationSaliency Diversified Deep Ensemble for Robustness to Adversaries
Deep learning models have shown incredible performance on numerous image recognition, classification, and reconstruction tasks. Although very appealing and valuable due to their predictive capabilities, one common threat…
DiversityCorrelation inference attacks against machine learning models
Despite machine learning models being widely used today, the relationship between a model and its training dataset is not well understood. We explore correlation inference attacks, whether and when a model leaks informat…
AttributeBIG-bench Machine LearningDecision MakingInference Attack+1Buffer Zone based Defense against Adversarial Examples in Image Classification
Recent defenses published at venues like NIPS, ICML, ICLR and CVPR are mainly focused on mitigating white-box attacks. These defenses do not properly consider adaptive adversaries. In this paper, we expand the scope of t…
Adversarial RobustnessClassificationGeneral Classificationimage-classification+1