ADVREPAIR:Provable Repair of Adversarial Attack

• URL: http://arxiv.org/abs/2404.01642v1
• Date: Tue, 2 Apr 2024 05:16:59 GMT
• Title: ADVREPAIR:Provable Repair of Adversarial Attack
• Authors: Zhiming Chi, Jianan Ma, Pengfei Yang, Cheng-Chao Huang, Renjue Li, Xiaowei Huang, Lijun Zhang,
• Abstract summary: Deep neural networks (DNNs) are increasingly deployed in safety-critical domains, but their vulnerability to adversarial attacks poses serious safety risks. Existing neuron-level methods using limited data lack efficacy in fixing adversaries due to the complexity of adversarial attack mechanisms. We propose ADVREPAIR, a novel approach for provable repair of adversarial attacks using limited data.
• Score: 15.580097790702508
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks (DNNs) are increasingly deployed in safety-critical domains, but their vulnerability to adversarial attacks poses serious safety risks. Existing neuron-level methods using limited data lack efficacy in fixing adversaries due to the inherent complexity of adversarial attack mechanisms, while adversarial training, leveraging a large number of adversarial samples to enhance robustness, lacks provability. In this paper, we propose ADVREPAIR, a novel approach for provable repair of adversarial attacks using limited data. By utilizing formal verification, ADVREPAIR constructs patch modules that, when integrated with the original network, deliver provable and specialized repairs within the robustness neighborhood. Additionally, our approach incorporates a heuristic mechanism for assigning patch modules, allowing this defense against adversarial attacks to generalize to other inputs. ADVREPAIR demonstrates superior efficiency, scalability and repair success rate. Different from existing DNN repair methods, our repair can generalize to general inputs, thereby improving the robustness of the neural network globally, which indicates a significant breakthrough in the generalization capability of ADVREPAIR.

Related papers

• REFINE: Inversion-Free Backdoor Defense via Model Reprogramming [60.554146386198376]
  Backdoor attacks on deep neural networks (DNNs) have emerged as a significant security threat. We propose REFINE, an inversion-free backdoor defense method based on model reprogramming.
  arXiv  Detail & Related papers  (2025-02-22T07:29:12Z)
• Real-world Adversarial Defense against Patch Attacks based on Diffusion Model [34.86098237949215]
  This paper introduces DIFFender, a novel DIFfusion-based DeFender framework to counter adversarial patch attacks. At the core of our approach is the discovery of the Adversarial Anomaly Perception (AAP) phenomenon. DIFFender seamlessly integrates the tasks of patch localization and restoration within a unified diffusion model framework.
  arXiv  Detail & Related papers  (2024-09-14T10:38:35Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• Improving the Robustness of Object Detection and Classification AI models against Adversarial Patch Attacks [2.963101656293054]
  We analyze attack techniques and propose a robust defense approach. We successfully reduce model confidence by over 20% using adversarial patch attacks that exploit object shape, texture and position. Our inpainting defense approach significantly enhances model resilience, achieving high accuracy and reliable localization despite the adversarial attacks.
  arXiv  Detail & Related papers  (2024-03-04T13:32:48Z)
• Scaling #DNN-Verification Tools with Efficient Bound Propagation and Parallel Computing [57.49021927832259]
  Deep Neural Networks (DNNs) are powerful tools that have shown extraordinary results in many scenarios. However, their intricate designs and lack of transparency raise safety concerns when applied in real-world applications. Formal Verification (FV) of DNNs has emerged as a valuable solution to provide provable guarantees on the safety aspect.
  arXiv  Detail & Related papers  (2023-12-10T13:51:25Z)
• Enumerating Safe Regions in Deep Neural Networks with Provable Probabilistic Guarantees [86.1362094580439]
  We introduce the AllDNN-Verification problem: given a safety property and a DNN, enumerate the set of all the regions of the property input domain which are safe. Due to the #P-hardness of the problem, we propose an efficient approximation method called epsilon-ProVe. Our approach exploits a controllable underestimation of the output reachable sets obtained via statistical prediction of tolerance limits.
  arXiv  Detail & Related papers  (2023-08-18T22:30:35Z)
• FACADE: A Framework for Adversarial Circuit Anomaly Detection and Evaluation [9.025997629442896]
  FACADE is designed for unsupervised mechanistic anomaly detection in deep neural networks. Our approach seeks to improve model robustness, enhance scalable model oversight, and demonstrates promising applications in real-world deployment settings.
  arXiv  Detail & Related papers  (2023-07-20T04:00:37Z)
• A Robust Optimisation Perspective on Counterexample-Guided Repair of Neural Networks [2.82532357999662]
  We show that counterexample-guided repair can be viewed as a robust optimisation algorithm. We prove termination for more restrained machine learning models and disprove termination in a general setting.
  arXiv  Detail & Related papers  (2023-01-26T19:00:02Z)
• Automated Repair of Neural Networks [0.26651200086513094]
  We introduce a framework for repairing unsafe NNs w.r.t. safety specification. Our method is able to search for a new, safe NN representation, by modifying only a few of its weight values. We perform extensive experiments which demonstrate the capability of our proposed framework to yield safe NNs w.r.t.
  arXiv  Detail & Related papers  (2022-07-17T12:42:24Z)
• Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement [79.2994130944482]
  We design a Domain Disentanglement Faster-RCNN (DDF) to eliminate the source-specific information in the features for detection task learning. Our DDF method facilitates the feature disentanglement at the global and local stages, with a Global Triplet Disentanglement (GTD) module and an Instance Similarity Disentanglement (ISD) module. By outperforming state-of-the-art methods on four benchmark UDA object detection tasks, our DDF method is demonstrated to be effective with wide applicability.
  arXiv  Detail & Related papers  (2022-01-06T05:43:01Z)
• ArchRepair: Block-Level Architecture-Oriented Repairing for Deep Neural Networks [13.661704974188872]
  We propose a novel repairing direction for deep neural networks (DNNs) at the block level. We propose adversarial-aware spectrum analysis for vulnerable block localization. We also propose the architecture-oriented search-based repairing that relaxes the targeted block to a continuous repairing search space.
  arXiv  Detail & Related papers  (2021-11-26T06:35:15Z)
• Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness [53.094682754683255]
  We propose a Model-Agnostic Meta-Attack (MAMA) approach to discover stronger attack algorithms automatically. Our method learns the in adversarial attacks parameterized by a recurrent neural network. We develop a model-agnostic training algorithm to improve the ability of the learned when attacking unseen defenses.
  arXiv  Detail & Related papers  (2021-10-13T13:54:24Z)
• The Feasibility and Inevitability of Stealth Attacks [63.14766152741211]
  We study new adversarial perturbations that enable an attacker to gain control over decisions in generic Artificial Intelligence systems. In contrast to adversarial data modification, the attack mechanism we consider here involves alterations to the AI system itself.
  arXiv  Detail & Related papers  (2021-06-26T10:50:07Z)
• Policy Smoothing for Provably Robust Reinforcement Learning [109.90239627115336]
  We study the provable robustness of reinforcement learning against norm-bounded adversarial perturbations of the inputs. We generate certificates that guarantee that the total reward obtained by the smoothed policy will not fall below a certain threshold under a norm-bounded adversarial of perturbation the input.
  arXiv  Detail & Related papers  (2021-06-21T21:42:08Z)
• Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design [76.29738151117583]
  Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients. However, low quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training. We model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk.
  arXiv  Detail & Related papers  (2021-05-10T08:02:27Z)
• Provable Repair of Deep Neural Networks [8.55884254206878]
  Deep Neural Networks (DNNs) have grown in popularity over the past decade and are now being used in safety-critical domains such as aircraft collision avoidance. This paper tackles the problem of correcting a DNN once unsafe behavior is found. We introduce the provable repair problem, which is the problem of repairing a network N to construct a new network N' that satisfies a given specification.
  arXiv  Detail & Related papers  (2021-04-09T15:03:53Z)
• NNrepair: Constraint-based Repair of Neural Network Classifiers [10.129874872336762]
  NNrepair is a constraint-based technique for repairing neural network classifiers. NNrepair first uses fault localization to find potentially faulty network parameters. It then performs repair using constraint solving to apply small modifications to the parameters to remedy the defects.
  arXiv  Detail & Related papers  (2021-03-23T13:44:01Z)
• A Data Augmentation-based Defense Method Against Adversarial Attacks in Neural Networks [7.943024117353317]
  We develop a lightweight defense method that can efficiently invalidate full whitebox adversarial attacks with the compatibility of real-life constraints. Our model can withstand advanced adaptive attack, namely BPDA with 50 rounds, and still helps the target model maintain an accuracy around 80 %, meanwhile constraining the attack success rate to almost zero.
  arXiv  Detail & Related papers  (2020-07-30T08:06:53Z)
• A Self-supervised Approach for Adversarial Robustness [105.88250594033053]
  Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems. This paper proposes a self-supervised adversarial training mechanism in the input space. It provides significant robustness against the textbfunseen adversarial attacks.
  arXiv  Detail & Related papers  (2020-06-08T20:42:39Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

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.