Error-correcting output codes (ECOC) is an ensemble method combining a set of binary
classifiers for multi-class learning problems. However, in traditional ECOC framework, the …

Though deep learning has been applied successfully in many scenarios, malicious inputs
with human-imperceptible perturbations can make it vulnerable in real applications. This …

Deep neural networks are vulnerable to adversarial attacks which can fool them by adding
minuscule perturbations to the input images. The robustness of existing defenses suffers …

Robustness of convolutional neural networks (CNNs) has gained in importance on account
of adversarial examples, ie, inputs added as well-designed perturbations that are …

Robustness of convolutional neural networks has recently been highlighted by the
adversarial examples, ie, inputs added with well-designed perturbations which are …

Recent studies have shown that deep neural networks are vulnerable to adversarial
examples, but most of the methods proposed to defense adversarial examples cannot solve …

In recent years, deep learning has shown impressive performance on many tasks. However,
recent researches showed that deep learning systems are vulnerable to small, specially …

Deep learning has achieved great results in the last decade, however, it is sensitive to so
called adversarial attacks-small perturbations of the input that cause the network to classify …

Error-Correcting Output Codes (ECOCs) have been proposed to construct multi-class
classifiers using simple binary classifiers. Recently, the principle of ECOCs has been …

Whereas adversarial training is employed as the main defence strategy against specific
adversarial samples, it has limited generalization capability and incurs excessive time …