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Found 16 papers, 9 papers with code
Blind-Match: Efficient Homomorphic Encryption-Based 1:N Matching for Privacy-Preserving Biometric Identification
We present Blind-Match, a novel biometric identification system that leverages homomorphic encryption (HE) for efficient and privacy-preserving 1:N matching.
AdFlush: A Real-World Deployable Machine Learning Solution for Effective Advertisement and Web Tracker Prevention
Conventional ad blocking and tracking prevention tools often fall short in addressing web content manipulation.
Expectations Versus Reality: Evaluating Intrusion Detection Systems in Practice
Our paper provides empirical comparisons between recent IDSs to provide an objective comparison between them to help users choose the most appropriate solution based on their requirements.
Single-Class Target-Specific Attack against Interpretable Deep Learning Systems
The universal perturbation is stochastically and iteratively optimized by minimizing the adversarial loss that is designed to consider both the classifier and interpreter costs in targeted and non-targeted categories.
DeepTaster: Adversarial Perturbation-Based Fingerprinting to Identify Proprietary Dataset Use in Deep Neural Networks
In this study, we introduce DeepTaster, a novel DNN fingerprinting technique, to address scenarios where a victim's data is unlawfully used to build a suspect model.
Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical World
The averaged ASR still remains sufficiently high to be 78% in the transfer learning attack scenarios evaluated on CenterNet.
Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Things
Federated learning (FL) and split learning (SL) are state-of-the-art distributed machine learning techniques to enable machine learning training without accessing raw data on clients or end devices.
Peeler: Profiling Kernel-Level Events to Detect Ransomware
Based on those characteristics, we develop Peeler that continuously monitors a target system's kernel events and detects ransomware attacks on the system.
Malware Detection
Cryptography and Security
DeepiSign: Invisible Fragile Watermark to Protect the Integrityand Authenticity of CNN
In this paper, we propose a self-contained tamper-proofing method, called DeepiSign, to ensure the integrity and authenticity of CNN models against such manipulation attacks.
Decamouflage: A Framework to Detect Image-Scaling Attacks on Convolutional Neural Networks
To corroborate the efficiency of Decamouflage, we have also measured its run-time overhead on a personal PC with an i5 CPU and found that Decamouflage can detect image-scaling attacks in milliseconds.
Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review
We have also reviewed the flip side of backdoor attacks, which are explored for i) protecting intellectual property of deep learning models, ii) acting as a honeypot to catch adversarial example attacks, and iii) verifying data deletion requested by the data contributor. Overall, the research on defense is far behind the attack, and there is no single defense that can prevent all types of backdoor attacks.
DeepCapture: Image Spam Detection Using Deep Learning and Data Augmentation
To show the feasibility of DeepCapture, we evaluate its performance with publicly available datasets consisting of 6, 000 spam and 2, 313 non-spam image samples.
Neural Network Laundering: Removing Black-Box Backdoor Watermarks from Deep Neural Networks
Creating a state-of-the-art deep-learning system requires vast amounts of data, expertise, and hardware, yet research into embedding copyright protection for neural networks has been limited.
End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things
For learning performance, which is specified by the model accuracy and convergence speed metrics, we empirically evaluate both FL and SplitNN under different types of data distributions such as imbalanced and non-independent and identically distributed (non-IID) data.
Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?
We observed that the 1D CNN model under split learning can achieve the same accuracy of 98. 9\% like the original (non-split) model.
Design and Evaluation of a Multi-Domain Trojan Detection Method on Deep Neural Networks
In particular, for vision tasks, we can always achieve a 0% FRR and FAR.
Cryptography and Security
