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Adversarial Images for Variational Autoencoders
[article]
2016
arXiv
pre-print
We investigate adversarial attacks for autoencoders. We propose a procedure that distorts the input image to mislead the autoencoder in reconstructing a completely different target image. We attack the internal latent representations, attempting to make the adversarial input produce an internal representation as similar as possible as the target's. We find that autoencoders are much more robust to the attack than classifiers: while some examples have tolerably small input distortion, and
arXiv:1612.00155v1
fatcat:esfo5uumq5h4ph6zrc5m6a7wvu