all InfoSec news
RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression. (arXiv:2203.10183v2 [cs.CV] UPDATED)
May 18, 2022, 1:20 a.m. | Jung-Woo Chang, Mojan Javaheripi, Seira Hidano, Farinaz Koushanfar
cs.CR updates on arXiv.org arxiv.org
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 paper, we conduct the first systematic study for
adversarial attacks on deep learning-based video compression and downstream
classification systems. Our attack framework, dubbed RoVISQ, manipulates the
Rate-Distortion (R-D) relationship of a video compression model to achieve one
or both of the following goals: (1) increasing the network bandwidth, (2)
degrading the video quality …
adversarial attacks compression deep learning quality service video
More from arxiv.org / cs.CR updates on arXiv.org
Jobs in InfoSec / Cybersecurity
SOC 2 Manager, Audit and Certification
@ Deloitte | US and CA Multiple Locations
Information Security Engineers
@ D. E. Shaw Research | New York City
Information Systems Security Officer (ISSO), Junior
@ Dark Wolf Solutions | Remote / Dark Wolf Locations
Cloud Security Engineer
@ ManTech | REMT - Remote Worker Location
SAP Security & GRC Consultant
@ NTT DATA | HYDERABAD, TG, IN
Security Engineer 2 - Adversary Simulation Operations
@ Datadog | New York City, USA