Rising concerns about privacy and anonymity preservation of deep learning models have
facilitated research in data-free learning. Primarily based on data-free knowledge distillation …

Deep neural networks have shown great potential in many practical applications, yet their
knowledge is at the risk of being stolen via exposed services (\eg APIs). In contrast to the …

With the development of deep learning, deep neural network (DNN)-based application have
become an indispensable aspect of daily life. However, recent studies have shown that …

This paper aims to craft adversarial queries for image retrieval, which uses image features
for similarity measurement. Many commonly used methods are developed in the context of …

Convolutional neural networks (CNNs) are known to be vulnerable to adversarial examples
even in black-box scenarios, posing a significant threat to their reliability and security. Most …

Since the training data for the target model in a data-free black-box attack is not available,
most recent schemes utilize GANs to generate data for training substitute model. However …

One area of current research on adversarial attacks is how to generate plausible adversarial
examples when only a small number of datasets are available. Current adversarial attack …

Adversarial examples are known to have the property of transferability; as a result, deep
neural networks can be compromised by transfer-based attacks in black-box scenarios …

Recent substitute training methods have utilized the concept of Generative Adversarial
Networks (GANs) to implement data-free black-box attacks. Specifically, in designing the …

Deep learning models are usually black boxes when deployed on machine learning
platforms. Prior works have shown that the attributes ($ eg $, the number of convolutional …