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Found 4 papers, 1 papers with code
Man-in-the-Middle Attacks against Machine Learning Classifiers via Malicious Generative Models
First, such attacks must acquire the outputs from the models by multiple times before actually launching attacks, which is difficult for the MitM adversary in practice.
Daedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples
Experimental results demonstrate that the attack effectively stops NMS from filtering redundant bounding boxes.
Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection
In contrast to existing works, the adversarial examples crafted by our method can also deceive recent machine learning based detectors that rely on semantic features such as control-flow-graph.
Cryptography and Security
Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-task Training
For example, proactive defending methods are invalid against grey-box or white-box attacks, while reactive defending methods are challenged by low-distortion adversarial examples or transferring adversarial examples.
