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Found 6 papers, 3 papers with code
Attacking and Defending Machine Learning Applications of Public Cloud
Adversarial attack breaks the boundaries of traditional security defense.
FastWordBug: A Fast Method To Generate Adversarial Text Against NLP Applications
In this paper, we present a novel algorithm, FastWordBug, to efficiently generate small text perturbations in a black-box setting that forces a sentiment analysis or text classification mode to make an incorrect prediction.
Advbox: a toolbox to generate adversarial examples that fool neural networks
In recent years, neural networks have been extensively deployed for computer vision tasks, particularly visual classification problems, where new algorithms reported to achieve or even surpass the human performance.
Transferability of Adversarial Examples to Attack Cloud-based Image Classifier Service
Fortunately, generating adversarial examples usually requires white-box access to the victim model, and real-world cloud-based image classification services are more complex than white-box classifier, the architecture and parameters of DL models on cloud platforms cannot be obtained by the attacker.
Improving Adversarial Robustness via Attention and Adversarial Logit Pairing
Finally, we conduct extensive experiments using a wide range of datasets and the experiment results show that our AT+ALP achieves the state of the art defense performance.
Cloud-based Image Classification Service Is Not Robust To Simple Transformations: A Forgotten Battlefield
Many recent works demonstrated that Deep Learning models are vulnerable to adversarial examples. Fortunately, generating adversarial examples usually requires white-box access to the victim model, and the attacker can only access the APIs opened by cloud platforms.
