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Creators/Authors contains: "Mohsenin, Tinoosh"

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  1. ; ; ; ; ( , 22nd International Symposium on Quality Electronic Design (ISQED))
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    This paper proposes an ensemble learning model that is resistant to adversarial attacks. To build resilience, we introduced a training process where each member learns a radically distinct latent space. Member models are added one at a time to the ensemble. Simultaneously, the loss function is regulated by a reverse knowledge distillation, forcing the new member to learn different features and map to a latent space safely distanced from those of existing members. We assessed the security and performance of the proposed solution on image classification tasks using CIFAR10 and MNIST datasets and showed security and performance improvement compared to the state of the art defense methods. 
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  2. ; ; ; ; ; ; ; ( , Proceedings of 2020 Great Lakes Symposium on VLSI (GLSVLSI'20))
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  3. ; ; ; ; ; ; ; ; ( , 2020 21st International Symposium on Quality Electronic Design (ISQED))
    null (Ed.)
    In this paper, we propose Code-Bridged Classifier (CBC), a framework for making a Convolutional Neural Network (CNNs) robust against adversarial attacks without increasing or even by decreasing the overall models' computational complexity. More specifically, we propose a stacked encoder-convolutional model, in which the input image is first encoded by the encoder module of a denoising auto-encoder, and then the resulting latent representation (without being decoded) is fed to a reduced complexity CNN for image classification. We illustrate that this network not only is more robust to adversarial examples but also has a significantly lower computational complexity when compared to the prior art defenses. 
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