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Found 11 papers, 2 papers with code
A Robust Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via (De)Randomized Smoothing
Our findings reveal that the chunk-based smoothing classifiers exhibit greater resilience against adversarial malware examples generated with state-of-the-are evasion attacks, outperforming a non-smoothed classifier and a randomized smoothing-based classifier by a great margin.
Differentially Private and Adversarially Robust Machine Learning: An Empirical Evaluation
Malicious adversaries can attack machine learning models to infer sensitive information or damage the system by launching a series of evasion attacks.
Domain Adaptation for Time series Transformers using One-step fine-tuning
To address these limitations, in this paper, we pre-train the time series Transformer model on a source domain with sufficient data and fine-tune it on the target domain with limited data.
Elevating Defenses: Bridging Adversarial Training and Watermarking for Model Resilience
We use adversarial training together with adversarial watermarks to train a robust watermarked model.
Towards a Practical Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via Randomized Smoothing
Malware detectors based on deep learning (DL) have been shown to be susceptible to malware examples that have been deliberately manipulated in order to evade detection, a. k. a.
Matching Pairs: Attributing Fine-Tuned Models to their Pre-Trained Large Language Models
The wide applicability and adaptability of generative large language models (LLMs) has enabled their rapid adoption.
Robust Learning Protocol for Federated Tumor Segmentation Challenge
In this work, we devise robust and efficient learning protocols for orchestrating a Federated Learning (FL) process for the Federated Tumor Segmentation Challenge (FeTS 2022).
Certified Federated Adversarial Training
We model an attacker who poisons the model to insert a weakness into the adversarial training such that the model displays apparent adversarial robustness, while the attacker can exploit the inserted weakness to bypass the adversarial training and force the model to misclassify adversarial examples.
FAT: Federated Adversarial Training
Federated learning (FL) is one of the most important paradigms addressing privacy and data governance issues in machine learning (ML).
Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systems
In the continuous data domain our attack successfully hides the cyber-physical attacks requiring 2. 87 out of 12 monitored sensors to be compromised on average.
Deep Latent Defence
The level of perturbation an attacker needs to introduce in order to cause such a misclassification can be extremely small, and often imperceptible.
