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Found 5 papers, 1 papers with code
LiveTune: Dynamic Parameter Tuning for Training Deep Neural Networks
Traditional machine learning training is a static process that lacks real-time adaptability of hyperparameters.
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.
Tailor: Altering Skip Connections for Resource-Efficient Inference
We argue that while a network's skip connections are needed for the network to learn, they can later be removed or shortened to provide a more hardware efficient implementation with minimal to no accuracy loss.
FastStamp: Accelerating Neural Steganography and Digital Watermarking of Images on FPGAs
In this work, we design the first accelerator platform FastStamp to perform DNN based steganography and digital watermarking of images on hardware.
zPROBE: Zero Peek Robustness Checks for Federated Learning
However, keeping the individual updates private allows malicious users to perform Byzantine attacks and degrade the accuracy without being detected.
