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Found 13 papers, 4 papers with code
A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworks
Federated learning (FL) is a distributed machine learning (ML) paradigm, allowing multiple clients to collaboratively train shared machine learning (ML) models without exposing clients' data privacy.
AS2T: Arbitrary Source-To-Target Adversarial Attack on Speaker Recognition Systems
Recent work has illuminated the vulnerability of speaker recognition systems (SRSs) against adversarial attacks, raising significant security concerns in deploying SRSs.
Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition
According to the characteristic of SRSs, we present 22 diverse transformations and thoroughly evaluate them using 7 recent promising adversarial attacks (4 white-box and 3 black-box) on speaker recognition.
Talking Head Generation Driven by Speech-Related Facial Action Units and Audio- Based on Multimodal Representation Fusion
We utilize pre-trained AU classifier to ensure that the generated images contain correct AU information.
Talking Head Generation with Audio and Speech Related Facial Action Units
Quantitative and qualitative experiments demonstrate that our method outperforms existing methods in both image quality and lip-sync accuracy.
SEC4SR: A Security Analysis Platform for Speaker Recognition
To bridge this gap, we present SEC4SR, the first platform enabling researchers to systematically and comprehensively evaluate adversarial attacks and defenses in SR. SEC4SR incorporates 4 white-box and 2 black-box attacks, 24 defenses including our novel feature-level transformations.
Why an Android App is Classified as Malware? Towards Malware Classification Interpretation
In this paper, to fill this gap, we propose a novel and interpretable ML-based approach (named XMal) to classify malware with high accuracy and explain the classification result meanwhile.
Advanced Evasion Attacks and Mitigations on Practical ML-Based Phishing Website Classifiers
To launch attacks in the white- and grey-box scenarios, we also propose a sample-based collision attack to gain the knowledge of the target classifier.
CORE: Automating Review Recommendation for Code Changes
The hinge of accurate code review suggestion is to learn good representations for both code changes and reviews.
ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking
Moreover, although generation models have the advantages of synthesizing commit messages for new code changes, they are not easy to bridge the semantic gap between code and natural languages which could be mitigated by retrieval models.
Software Engineering
Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems
In this paper, we conduct the first comprehensive and systematic study of the adversarial attacks on SR systems (SRSs) to understand their security weakness in the practical blackbox setting.
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms
However, the differences in architecture designs and implementations of existing frameworks and platforms bring new challenges for DL software development and deployment.
Secure Deep Learning Engineering: A Software Quality Assurance Perspective
Over the past decades, deep learning (DL) systems have achieved tremendous success and gained great popularity in various applications, such as intelligent machines, image processing, speech processing, and medical diagnostics.
