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

Team

• Jung-Woo Chang, Ph.D student, ECE, University of California San Diego

• Mojan Javaheripi, Ph.D student, ECE, University of California San Diego

• Seira Hidano, KDDI Research, Inc.

• Farinaz Koushanfar, Professor, ECE, University of California San Diego

Overview

We present RoVISQ, Reduction of Video Service Quality, as a new threat to video compression. We show that DNN-based video compression systems can be exploited by adversaries to not only reduce the users’ quality of experience (QoE), but also attack downstream video recognition services. 

Attacker's Goal

• The adversary’s goal is to selectively degrade the bit-rate R and/or distortion level D compared to a pre-trained R-D curve for the video encoder and decoder. 

• We introduce the following RoVISQ attacks:

  1. RD attack

  2. Video quality attack

  3. Bandwidth attack

  4. Compression-robust classifier attack

Attack Methodology

• We conduct the first systematic study for adversarial attacks on deep learning-based video compression and downstream classification systems

• RoVISQ attacks are targeted towards video data generated by front-end sources.

• We formalize RoVISQ attacks as well-defined optimization problems.

• By solving the problem, we can obtain perturbations affecting the R, D, and downstream classification.

Attacked Video Sequences

1. PartyScene

2. BQTerrace

3. BasketballDrill

4. RaceHorses

5. BQMall

6. Cactus

Citation

[NDSS'23] RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression

Jung-Woo Chang, Mojan Javaheripi, Seira Hidano, and Farinaz Koushanfar

To appear in the 30th Network and Distributed System Security (NDSS) Symposium, San Diego, USA, 2023. (acceptance rate=16.2%) 

@article{chang2022rovisq,

  title={RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression},

  author={Chang, Jung-Woo and Javaheripi, Mojan and Hidano, Seira and Koushanfar, Farinaz},

  booktitle={Proceedings 2023 Network and Distributed System Security Symposium. NDSS},

  year={2023}

}

Related Works

• A curated list of Adversarial Meachine Learning (AML) papers on Neural Compression published in top conferences: Link