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Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems
Although there have been a number of adversarial attacks on ML-based wireless systems, the existing methods do not provide a comprehensive view including multi-modality of the source data, common physical layer components, and wireless domain constraints.
NetFlick: Adversarial Flickering Attacks on Deep Learning Based Video Compression
Experimental results demonstrate that NetFlick can successfully deteriorate the performance of video compression frameworks in both digital- and physical-settings and can be further extended to attack downstream video classification networks.
RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression
In this paper, we conduct the first systematic study for adversarial attacks on deep learning-based video compression and downstream classification systems.
An Efficient Accelerator Design Methodology for Deformable Convolutional Networks
In this paper, we present a novel approach to accelerate deformable convolution on FPGA.
Towards Design Methodology of Efficient Fast Algorithms for Accelerating Generative Adversarial Networks on FPGAs
To implement the DeConv layer in hardware, the state-of-the-art accelerator reduces the high computational complexity via the DeConv-to-Conv conversion and achieves the same results.
