Search Results for author:
Found 7 papers, 4 papers with code
AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake Detection
While DeepFake applications are becoming popular in recent years, their abuses pose a serious privacy threat.
RNN-Guard: Certified Robustness Against Multi-frame Attacks for Recurrent Neural Networks
In this paper, we propose the first certified defense against multi-frame attacks for RNNs called RNN-Guard.
Label Inference Attacks Against Vertical Federated Learning
However, we discover that the bottom model structure and the gradient update mechanism of VFL can be exploited by a malicious participant to gain the power to infer the privately owned labels.
Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Era
Facial Liveness Verification (FLV) is widely used for identity authentication in many security-sensitive domains and offered as Platform-as-a-Service (PaaS) by leading cloud vendors.
SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems
In this paper, we present our systematization of knowledge for ASR security and provide a comprehensive taxonomy for existing work based on a modularized workflow.
Learning Symmetric and Asymmetric Steganography via Adversarial Training
Steganography refers to the art of concealing secret messages within multiple media carriers so that an eavesdropper is unable to detect the presence and content of the hidden messages.
How to Prove Your Model Belongs to You: A Blind-Watermark based Framework to Protect Intellectual Property of DNN
To fill these gaps, in this paper, we propose a novel intellectual property protection (IPP) framework based on blind-watermark for watermarking deep neural networks that meet the requirements of security and feasibility.
