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20152026
most citedXONN: XNOR-based Oblivious Deep Neural Network Inference

157 citations · 929 across the 89 of their papers we have counts for

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7 papers · 1 filter

cs.CV2023★ 2 cited

Tailor: Altering Skip Connections for Resource-Efficient Inference

Olivia Weng, Gabriel Marcano, Vladimir Loncar +7

Deep neural networks use skip connections to improve training convergence. However, these skip connections are costly in hardware, requiring extra buffers and increasing on- and of…

cs.CV2022★ 7 cited

FastStamp: Accelerating Neural Steganography and Digital Watermarking of Images on FPGAs

Shehzeen Hussain, Nojan Sheybani, Paarth Neekhara +3

Steganography and digital watermarking are the tasks of hiding recoverable data in image pixels. Deep neural network (DNN) based image steganography and watermarking techniques are…

cs.CV2022★ 4 cited

ReFace: Real-time Adversarial Attacks on Face Recognition Systems

Shehzeen Hussain, Todd Huster, Chris Mesterharm +5

Deep neural network based face recognition models have been shown to be vulnerable to adversarial examples. However, many of the past attacks require the adversary to solve an inpu…

cs.CV2022★ 8 cited

FaceSigns: Semi-Fragile Neural Watermarks for Media Authentication and Countering Deepfakes

Paarth Neekhara, Shehzeen Hussain, Xinqiao Zhang +3

Deepfakes and manipulated media are becoming a prominent threat due to the recent advances in realistic image and video synthesis techniques. There have been several attempts at co…

cs.CV2022★ 6 cited

RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression

Jung-Woo Chang, Mojan Javaheripi, Seira Hidano +1

Video compression plays a crucial role in video streaming and classification systems by maximizing the end-user quality of experience (QoE) at a given bandwidth budget. In this pap…

cs.CV2020★ 3 cited

A Singular Value Perspective on Model Robustness

Malhar Jere, Maghav Kumar, Farinaz Koushanfar

Convolutional Neural Networks (CNNs) have made significant progress on several computer vision benchmarks, but are fraught with numerous non-human biases such as vulnerability to a…

cs.CV2020★ 2 cited

Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples

Shehzeen Hussain, Paarth Neekhara, Malhar Jere +2

Recent advances in video manipulation techniques have made the generation of fake videos more accessible than ever before. Manipulated videos can fuel disinformation and reduce tru…