Add to ORKGThe paper presents a new defense against adversarial attacks for deep neural networks. We demonstrate the effectiveness of our approach against the popular adversarial image generation method DeepFool. Our approach uses Wald’s Sequential Probability Ratio Test to sufficiently sample a carefully chosen neighborhood around an input image to determine the correct label of the image. On a benchmark of 50,000 randomly chosen adversarial images generated by DeepFool we demonstrate that our method SAT YA is able to recover the correct labels for 95.76% of the images for CaffeNet and 97.43% of the correct label for GoogLeNet
In recent years, adversarial attack methods have been deceived rather easily on deep neural networks...
Deep learning has achieved great successes in various types of applications over recent years. On th...
A growing body of work has shown that deep neural networks are susceptible to adversarial examples. ...
Although Deep Neural Networks (DNNs) have achieved great success on various applications, investigat...
Although Deep Neural Networks (DNNs) have achieved great success on various applications, investigat...
DeepNeuralNetworks (DNNs) are powerful to the classification tasks, finding the potential links bet...
DeepNeuralNetworks (DNNs) are powerful to the classification tasks, finding the potential links bet...
Deep neural networks (DNNs) provide excellent performance in image recognition, speech recognition, ...
As modern technology is rapidly progressing, more applications are utilizing aspects of machine lear...
As modern technology is rapidly progressing, more applications are utilizing aspects of machine lear...
Deep neural networks are susceptible to interference from deliberately crafted noise, which can lead...
Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neu...
Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neu...
Deep Neural Networks (DNNs) are adept at many tasks, with the more well-known task of image recognit...
Deep Neural Networks (DNNs) are adept at many tasks, with the more well-known task of image recognit...
In recent years, adversarial attack methods have been deceived rather easily on deep neural networks...
Deep learning has achieved great successes in various types of applications over recent years. On th...
A growing body of work has shown that deep neural networks are susceptible to adversarial examples. ...
Although Deep Neural Networks (DNNs) have achieved great success on various applications, investigat...
Although Deep Neural Networks (DNNs) have achieved great success on various applications, investigat...
DeepNeuralNetworks (DNNs) are powerful to the classification tasks, finding the potential links bet...
DeepNeuralNetworks (DNNs) are powerful to the classification tasks, finding the potential links bet...
Deep neural networks (DNNs) provide excellent performance in image recognition, speech recognition, ...
As modern technology is rapidly progressing, more applications are utilizing aspects of machine lear...
As modern technology is rapidly progressing, more applications are utilizing aspects of machine lear...
Deep neural networks are susceptible to interference from deliberately crafted noise, which can lead...
Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neu...
Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neu...
Deep Neural Networks (DNNs) are adept at many tasks, with the more well-known task of image recognit...
Deep Neural Networks (DNNs) are adept at many tasks, with the more well-known task of image recognit...
In recent years, adversarial attack methods have been deceived rather easily on deep neural networks...
Deep learning has achieved great successes in various types of applications over recent years. On th...
A growing body of work has shown that deep neural networks are susceptible to adversarial examples. ...