Mitigating the Impact of Adversarial Attacks in Very Deep Networks
- URL: http://arxiv.org/abs/2012.04750v1
- Date: Tue, 8 Dec 2020 21:25:44 GMT
- Title: Mitigating the Impact of Adversarial Attacks in Very Deep Networks
- Authors: Mohammed Hassanin, Ibrahim Radwan, Nour Moustafa, Murat Tahtali,
Neeraj Kumar
- Abstract summary: Deep Neural Network (DNN) models have vulnerabilities related to security concerns.
Data poisoning-enabled perturbation attacks are complex adversarial ones that inject false data into models.
We propose an attack-agnostic-based defense method for mitigating their influence.
- Score: 10.555822166916705
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Deep Neural Network (DNN) models have vulnerabilities related to security
concerns, with attackers usually employing complex hacking techniques to expose
their structures. Data poisoning-enabled perturbation attacks are complex
adversarial ones that inject false data into models. They negatively impact the
learning process, with no benefit to deeper networks, as they degrade a model's
accuracy and convergence rates. In this paper, we propose an
attack-agnostic-based defense method for mitigating their influence. In it, a
Defensive Feature Layer (DFL) is integrated with a well-known DNN architecture
which assists in neutralizing the effects of illegitimate perturbation samples
in the feature space. To boost the robustness and trustworthiness of this
method for correctly classifying attacked input samples, we regularize the
hidden space of a trained model with a discriminative loss function called
Polarized Contrastive Loss (PCL). It improves discrimination among samples in
different classes and maintains the resemblance of those in the same class.
Also, we integrate a DFL and PCL in a compact model for defending against data
poisoning attacks. This method is trained and tested using the CIFAR-10 and
MNIST datasets with data poisoning-enabled perturbation attacks, with the
experimental results revealing its excellent performance compared with those of
recent peer techniques.
Related papers
- MOREL: Enhancing Adversarial Robustness through Multi-Objective Representation Learning [1.534667887016089]
deep neural networks (DNNs) are vulnerable to slight adversarial perturbations.
We show that strong feature representation learning during training can significantly enhance the original model's robustness.
We propose MOREL, a multi-objective feature representation learning approach, encouraging classification models to produce similar features for inputs within the same class, despite perturbations.
arXiv Detail & Related papers (2024-10-02T16:05:03Z) - Celtibero: Robust Layered Aggregation for Federated Learning [0.0]
We introduce Celtibero, a novel defense mechanism that integrates layered aggregation to enhance robustness against adversarial manipulation.
We demonstrate that Celtibero consistently achieves high main task accuracy (MTA) while maintaining minimal attack success rates (ASR) across a range of untargeted and targeted poisoning attacks.
arXiv Detail & Related papers (2024-08-26T12:54:00Z) - FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning
Attacks in Federated Learning [98.43475653490219]
Federated learning (FL) is susceptible to poisoning attacks.
FreqFed is a novel aggregation mechanism that transforms the model updates into the frequency domain.
We demonstrate that FreqFed can mitigate poisoning attacks effectively with a negligible impact on the utility of the aggregated model.
arXiv Detail & Related papers (2023-12-07T16:56:24Z) - DALA: A Distribution-Aware LoRA-Based Adversarial Attack against
Language Models [64.79319733514266]
Adversarial attacks can introduce subtle perturbations to input data.
Recent attack methods can achieve a relatively high attack success rate (ASR)
We propose a Distribution-Aware LoRA-based Adversarial Attack (DALA) method.
arXiv Detail & Related papers (2023-11-14T23:43:47Z) - DAD++: Improved Data-free Test Time Adversarial Defense [12.606555446261668]
We propose a test time Data-free Adversarial Defense (DAD) containing detection and correction frameworks.
We conduct a wide range of experiments and ablations on several datasets and network architectures to show the efficacy of our proposed approach.
Our DAD++ gives an impressive performance against various adversarial attacks with a minimal drop in clean accuracy.
arXiv Detail & Related papers (2023-09-10T20:39:53Z) - Towards Attack-tolerant Federated Learning via Critical Parameter
Analysis [85.41873993551332]
Federated learning systems are susceptible to poisoning attacks when malicious clients send false updates to the central server.
This paper proposes a new defense strategy, FedCPA (Federated learning with Critical Analysis)
Our attack-tolerant aggregation method is based on the observation that benign local models have similar sets of top-k and bottom-k critical parameters, whereas poisoned local models do not.
arXiv Detail & Related papers (2023-08-18T05:37:55Z) - Avoid Adversarial Adaption in Federated Learning by Multi-Metric
Investigations [55.2480439325792]
Federated Learning (FL) facilitates decentralized machine learning model training, preserving data privacy, lowering communication costs, and boosting model performance through diversified data sources.
FL faces vulnerabilities such as poisoning attacks, undermining model integrity with both untargeted performance degradation and targeted backdoor attacks.
We define a new notion of strong adaptive adversaries, capable of adapting to multiple objectives simultaneously.
MESAS is the first defense robust against strong adaptive adversaries, effective in real-world data scenarios, with an average overhead of just 24.37 seconds.
arXiv Detail & Related papers (2023-06-06T11:44:42Z) - FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated
Learning [66.56240101249803]
We study how hardening benign clients can affect the global model (and the malicious clients)
We propose a trigger reverse engineering based defense and show that our method can achieve improvement with guarantee robustness.
Our results on eight competing SOTA defense methods show the empirical superiority of our method on both single-shot and continuous FL backdoor attacks.
arXiv Detail & Related papers (2022-10-23T22:24:03Z) - FL-Defender: Combating Targeted Attacks in Federated Learning [7.152674461313707]
Federated learning (FL) enables learning a global machine learning model from local data distributed among a set of participating workers.
FL is vulnerable to targeted poisoning attacks that negatively impact the integrity of the learned model.
We propose textitFL-Defender as a method to combat FL targeted attacks.
arXiv Detail & Related papers (2022-07-02T16:04:46Z) - PiDAn: A Coherence Optimization Approach for Backdoor Attack Detection
and Mitigation in Deep Neural Networks [22.900501880865658]
Backdoor attacks impose a new threat in Deep Neural Networks (DNNs)
We propose PiDAn, an algorithm based on coherence optimization purifying the poisoned data.
Our PiDAn algorithm can detect more than 90% infected classes and identify 95% poisoned samples.
arXiv Detail & Related papers (2022-03-17T12:37:21Z) - Adversarial Self-Supervised Contrastive Learning [62.17538130778111]
Existing adversarial learning approaches mostly use class labels to generate adversarial samples that lead to incorrect predictions.
We propose a novel adversarial attack for unlabeled data, which makes the model confuse the instance-level identities of the perturbed data samples.
We present a self-supervised contrastive learning framework to adversarially train a robust neural network without labeled data.
arXiv Detail & Related papers (2020-06-13T08:24:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.