  Machine Learning using neural networks has received prominent attention
recently because of its success in solving a wide variety of computational
tasks, in particular in the field of computer vision. However, several works
have drawn attention to potential security risks involved with the training and
implementation of such networks. In this work, we introduce DeepBaR, a novel
approach that implants backdoors on neural networks by faulting their behavior
at training, especially during fine-tuning. Our technique aims to generate
adversarial samples by optimizing a custom loss function that mimics the
implanted backdoors while adding an almost non-visible trigger in the image. We
attack three popular convolutional neural network architectures and show that
DeepBaR attacks have a success rate of up to 98.30\%. Furthermore, DeepBaR does
not significantly affect the accuracy of the attacked networks after deployment
when non-malicious inputs are given. Remarkably, DeepBaR allows attackers to
choose an input that looks similar to a given class, from a human perspective,
but that will be classified as belonging to an arbitrary target class.
