Add to ORKGHigh-performance Deep Neural Networks (DNNs) are increasingly deployed in many real-world applications e.g., cloud prediction APIs. Recent advances in model functionality stealing attacks via black-box access (i.e., inputs in, predictions out) threaten the business model of such applications, which require a lot of time, money, and effort to develop. Existing defenses take a passive role against stealing attacks, such as by truncating predicted information. We find such passive defenses ineffective against DNN stealing attacks. In this paper, we propose the first defense which actively perturbs predictions targeted at poisoning the training objective of the attacker. We find our defense effective across a wide range of challenging datasets ...
Deep neural networks (DNNs) are widely deployed today, from image classification to voice recognitio...
Deep learning is a machine learning technique that enables computers to learn directly from images, ...
Deep neural network (DNN) has progressed rapidly during the past decade and DNN models have been dep...
High-performance Deep Neural Networks (DNNs) are increasingly deployed in many real-world applicatio...
Machine learning (ML) applications are increasingly prevalent. Protecting the confidentiality of ML ...
Deep neural networks (DNNs) have become the essential components for various commercialized machine ...
Machine learning models based on Deep Neural Networks (DNN) have gained popularity due to their pr...
Server breaches are an unfortunate reality on today's Internet. In the context of deep neural networ...
Deep Neural Networks (DNNs) have achieved great success in a wide range of applications, such as ima...
Model stealing attacks have become a serious concern for deep learning models, where an attacker can...
Benefiting from the advancement of algorithms in massive data and powerful computing resources, deep...
The vulnerability of deep neural networks to adversarial attacks has posed significant threats to re...
In standard Deep Neural Network (DNN) based classifiers, the general convention is to omit the activ...
Machine learning (ML) and deep learning methods have become common and publicly available, while ML ...
Graph data has been widely used to represent data from various domain, e.g., social networks, recomm...
Deep neural networks (DNNs) are widely deployed today, from image classification to voice recognitio...
Deep learning is a machine learning technique that enables computers to learn directly from images, ...
Deep neural network (DNN) has progressed rapidly during the past decade and DNN models have been dep...
High-performance Deep Neural Networks (DNNs) are increasingly deployed in many real-world applicatio...
Machine learning (ML) applications are increasingly prevalent. Protecting the confidentiality of ML ...
Deep neural networks (DNNs) have become the essential components for various commercialized machine ...
Machine learning models based on Deep Neural Networks (DNN) have gained popularity due to their pr...
Server breaches are an unfortunate reality on today's Internet. In the context of deep neural networ...
Deep Neural Networks (DNNs) have achieved great success in a wide range of applications, such as ima...
Model stealing attacks have become a serious concern for deep learning models, where an attacker can...
Benefiting from the advancement of algorithms in massive data and powerful computing resources, deep...
The vulnerability of deep neural networks to adversarial attacks has posed significant threats to re...
In standard Deep Neural Network (DNN) based classifiers, the general convention is to omit the activ...
Machine learning (ML) and deep learning methods have become common and publicly available, while ML ...
Graph data has been widely used to represent data from various domain, e.g., social networks, recomm...
Deep neural networks (DNNs) are widely deployed today, from image classification to voice recognitio...
Deep learning is a machine learning technique that enables computers to learn directly from images, ...
Deep neural network (DNN) has progressed rapidly during the past decade and DNN models have been dep...