Constructing an attack on models with heterogeneous input spaces is challenging, as they are governed by complex domain-specific validity rules and comprised of nominal, ordinal, and numerical features. We argue
The attack transferability enables adversarial examples to attack black-box DNNs with unknown archi- tectures or parameters, which poses threats to many real- world applications. We find that existing transferable at- tacks do not distinguish between style and content features ...
The natural and medical images are from CIFAR10 and CXRs, respectively. Transferable adversarial examples are generated by ResNet101 under the attack PGD-20. Feature distance is the Euclidean distance between original images and their adversarial examples under different perturbations, where features are...
suggesting fundamental flaws in their design. Ilyas et al.29propose that the existence of adversarial examples is due to ANNs exploiting features that are predictive but not causal, and perhaps ANNs are far more sensitive to these features than humans. Kim et al.30further argued that neural mech...
Deep neural networks are currently the most widespread and successful technology in artificial intelligence. However, these systems exhibit bewildering new vulnerabilities: most notably a susceptibility to adversarial examples. Here, I review recent empirical research on adversarial examples that suggests that...
Adversarial models involve two main components: a generator that generates data that attempts to fool the discriminator and a discriminator that distinguishes between artificially generated data and real data. In this example, you train an adversarial learning model using clean and noisy signals. The ...
(3) even when all attacks are bounded by the same Lp norm, they lead to dramatically different stealthiness performance, which negatively correlates with their transferability performance. We providethe first large-scale evaluation of transferable adversarial examples on ImageNet, involving 23 representat...
loc = matlab.internal.examples.downloadSupportFile("audio","examples/PercussiveSoundGenerator.zip"); unzip(loc,pwd) The supporting function synthesizePercussiveSound calls a pretrained network to synthesize a percussive sound sampled at 16 kHz. The synthesizePercussiveSound function is included at the en...
In many applications, how- ever, it is required that the adversarial examples are generated in the physical domain, producing real-world objects that, when sensed by the sensors of the attacked system and fed to the CNN, cause a misclassification error. In this setting, and by focusing on ...
Ilyas et al.29 propose that the existence of adversarial examples is due to ANNs exploiting features that are predictive but not causal, and perhaps ANNs are far more sensitive to these features than humans. Kim et al.30 further argued that neural mechanisms in the human visual pathway may ...