Unreasonable effectiveness of last hidden layer activations for adversarial robustness
Yükleniyor...
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In standard Deep Neural Network (DNN) based classifiers, the general convention is to omit the activation function in the last (output) layer and directly apply the softmax function on the logits to get the probability scores of each class. In this type of architectures, the loss value of the classifier against any output class is directly proportional to the difference between the final probability score and the label value of the associated class. Standard White-box adversarial evasion attacks, whether targeted or untargeted, mainly try to exploit the gradient of the model loss function to craft adversarial samples and fool the model. In this study, we show both mathematically and experimentally that using some widely known activation functions in the output layer of the model with high temperature values has the effect of zeroing out the gradients for both targeted and untargeted attack cases, preventing attackers from exploiting the model's loss function to craft adversarial samples. We've experimentally verified the efficacy of our approach on MNIST (Digit), CIFAR10 datasets. Detailed experiments confirmed that our approach substantially improves robustness against gradient-based targeted and untargeted attack threats. And, we showed that the increased non-linearity at the output layer has some ad-ditional benefits against some other attack methods like Deepfool attack.
Açıklama
Anahtar Kelimeler
Adversarial machine learning, Deep neural networks, Robustness, Trustworthy AI, Chemical activation, Multilayer neural networks, Activation functions, Hidden layers, Loss functions, Machine-learning, Network-based, Output layer, White box, Object detection, Deep learning, IOU
Kaynak
2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Tuna, Ö. F., Çatak, F. Ö. & Eskil, M. T. (2022). Unreasonable effectiveness of last hidden layer activations for adversarial robustness. Paper presented at the 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), 1098-1103. doi:10.1109/COMPSAC54236.2022.00172
