Few-Shot Personalized Saliency Prediction With Similarity of Gaze Tendency Using Object-Based Structural information

We study the hard-label adversarial attacks where model information, training data, and output score are all unknown except for the final decision to an input query. Due to the security issue, model providers usually constrain the number of queries. It is challenging for adversaries to attack a model with limited queries. Sign-OPT makes great advances in query complexity, which adopts the backtracking line-search to find the optimal search direction, meanwhile the binary search is employed to obtain the minimum-distortion adversarial example. We find that this binary search costs a huge amount of queries. This paper proposes an improved Sign-OPT, termed Sign-OPT+, to enhance query efficiency further. instead of the binary search, at each line-search stage we directly judge whether the candidate example along the new search direction locates inside or outside the decision boundary. This judgment requires only one query to achieve the optimal search direction, significantly reducing the overall queries. Experiments tested on MNIST, CIFAR-10, and ImageNet show that our Sign-OPT+ requires fewer queries and obtains a higher success rate than the state-of-the-art including Sign-OPT. The source code is available at https://github.com/GZHU-DVL/Sign-OPT-plus.