Ensemble-based Blackbox Attacks on Dense Prediction

• URL: http://arxiv.org/abs/2303.14304v1
• Date: Sat, 25 Mar 2023 00:08:03 GMT
• Title: Ensemble-based Blackbox Attacks on Dense Prediction
• Authors: Zikui Cai, Yaoteng Tan, M. Salman Asif
• Abstract summary: We show that a carefully designed ensemble can create effective attacks for a number of victim models. In particular, we show that normalization of the weights for individual models plays a critical role in the success of the attacks. Our proposed method can also generate a single perturbation that can fool multiple blackbox detection and segmentation models simultaneously.
• Score: 16.267479602370543
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an approach for adversarial attacks on dense prediction models (such as object detectors and segmentation). It is well known that the attacks generated by a single surrogate model do not transfer to arbitrary (blackbox) victim models. Furthermore, targeted attacks are often more challenging than the untargeted attacks. In this paper, we show that a carefully designed ensemble can create effective attacks for a number of victim models. In particular, we show that normalization of the weights for individual models plays a critical role in the success of the attacks. We then demonstrate that by adjusting the weights of the ensemble according to the victim model can further improve the performance of the attacks. We performed a number of experiments for object detectors and segmentation to highlight the significance of the our proposed methods. Our proposed ensemble-based method outperforms existing blackbox attack methods for object detection and segmentation. Finally we show that our proposed method can also generate a single perturbation that can fool multiple blackbox detection and segmentation models simultaneously. Code is available at https://github.com/CSIPlab/EBAD.

Related papers

• Defense Against Model Extraction Attacks on Recommender Systems [53.127820987326295]
  We introduce Gradient-based Ranking Optimization (GRO) to defend against model extraction attacks on recommender systems. GRO aims to minimize the loss of the protected target model while maximizing the loss of the attacker's surrogate model. Results show GRO's superior effectiveness in defending against model extraction attacks.
  arXiv  Detail & Related papers  (2023-10-25T03:30:42Z)
• Understanding the Robustness of Randomized Feature Defense Against Query-Based Adversarial Attacks [23.010308600769545]
  Deep neural networks are vulnerable to adversarial examples that find samples close to the original image but can make the model misclassify. We propose a simple and lightweight defense against black-box attacks by adding random noise to hidden features at intermediate layers of the model at inference time. Our method effectively enhances the model's resilience against both score-based and decision-based black-box attacks.
  arXiv  Detail & Related papers  (2023-10-01T03:53:23Z)
• Certifiable Black-Box Attacks with Randomized Adversarial Examples: Breaking Defenses with Provable Confidence [34.35162562625252]
  Black-box adversarial attacks have demonstrated strong potential to compromise machine learning models. We study a new paradigm of black-box attacks with provable guarantees. This new black-box attack unveils significant vulnerabilities of machine learning models.
  arXiv  Detail & Related papers  (2023-04-10T01:12:09Z)
• Object-fabrication Targeted Attack for Object Detection [54.10697546734503]
  adversarial attack for object detection contains targeted attack and untargeted attack. New object-fabrication targeted attack mode can mislead detectors tofabricate extra false objects with specific target labels.
  arXiv  Detail & Related papers  (2022-12-13T08:42:39Z)
• Query Efficient Cross-Dataset Transferable Black-Box Attack on Action Recognition [99.29804193431823]
  Black-box adversarial attacks present a realistic threat to action recognition systems. We propose a new attack on action recognition that addresses these shortcomings by generating perturbations. Our method achieves 8% and higher 12% deception rates compared to state-of-the-art query-based and transfer-based attacks.
  arXiv  Detail & Related papers  (2022-11-23T17:47:49Z)
• T-SEA: Transfer-based Self-Ensemble Attack on Object Detection [9.794192858806905]
  We propose a single-model transfer-based black-box attack on object detection, utilizing only one model to achieve a high-transferability adversarial attack on multiple black-box detectors. We analogize patch optimization with regular model optimization, proposing a series of self-ensemble approaches on the input data, the attacked model, and the adversarial patch.
  arXiv  Detail & Related papers  (2022-11-16T10:27:06Z)
• Parallel Rectangle Flip Attack: A Query-based Black-box Attack against Object Detection [89.08832589750003]
  We propose a Parallel Rectangle Flip Attack (PRFA) via random search to avoid sub-optimal detection near the attacked region. Our method can effectively and efficiently attack various popular object detectors, including anchor-based and anchor-free, and generate transferable adversarial examples.
  arXiv  Detail & Related papers  (2022-01-22T06:00:17Z)
• Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability [20.255708227671573]
  Black-box adversarial attacks can be transferred from one model to another. In this work, we propose a novel ensemble attack method called the variance reduced ensemble attack. Empirical results on the standard ImageNet demonstrate that the proposed method could boost the adversarial transferability and outperforms existing ensemble attacks significantly.
  arXiv  Detail & Related papers  (2021-11-21T06:33:27Z)
• Towards A Conceptually Simple Defensive Approach for Few-shot classifiers Against Adversarial Support Samples [107.38834819682315]
  We study a conceptually simple approach to defend few-shot classifiers against adversarial attacks. We propose a simple attack-agnostic detection method, using the concept of self-similarity and filtering. Our evaluation on the miniImagenet (MI) and CUB datasets exhibit good attack detection performance.
  arXiv  Detail & Related papers  (2021-10-24T05:46:03Z)
• Can Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap? [36.96777303738315]
  We design blackbox transfer-based targeted adversarial attacks for an environment where the attacker's source model and the target blackbox model may have disjoint label spaces and training datasets. Our methodology begins with the construction of a class correspondence matrix between the whitebox and blackbox label sets. We show that our transfer attacks serve as powerful adversarial priors when integrated with query-based methods.
  arXiv  Detail & Related papers  (2021-03-17T21:21:44Z)
• Hidden Backdoor Attack against Semantic Segmentation Models [60.0327238844584]
  The emphbackdoor attack intends to embed hidden backdoors in deep neural networks (DNNs) by poisoning training data. We propose a novel attack paradigm, the emphfine-grained attack, where we treat the target label from the object-level instead of the image-level. Experiments show that the proposed methods can successfully attack semantic segmentation models by poisoning only a small proportion of training data.
  arXiv  Detail & Related papers  (2021-03-06T05:50:29Z)

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.