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Attacking No-Reference Image Quality Assessment Models: Disrupting Both Score Changes and Ranking Correlations


Core Concepts
The authors propose a novel correlation-error-based attack framework that can effectively disrupt both the predicted scores of individual images and the ranking correlation within an entire image set for no-reference image quality assessment (NR-IQA) models.
Abstract
The paper introduces a new framework of correlation-error-based attacks on NR-IQA models. Current adversarial attacks on NR-IQA models focus on perturbing the predicted scores of individual images, but neglect the crucial aspect of inter-score correlation relationships within an entire image set. The authors propose a two-stage SROCC-MSE Attack (SMA) method to address this gap. In Stage One, the objective is to identify optimal target scores that significantly reduce the Spearman's Rank-Order Correlation Coefficient (SROCC) and increase the Mean Squared Error (MSE) between the predicted scores of attacked and clean images. In Stage Two, adversarial examples are generated to make their predicted scores as close as possible to the target scores identified in Stage One. Extensive experiments on four widely-used NR-IQA models (DBCNN, HyperIQA, MANIQA, and LIQE) demonstrate that SMA not only significantly disrupts the SROCC to negative values but also maintains a considerable change in the scores of individual images. It outperforms four existing attack methods across various evaluation metrics, including error-based, correlation-based, and others. The findings underscore the vulnerability of NR-IQA models in maintaining both individual scores and correlations, paving the way for further research on developing more secure and robust NR-IQA models.
Stats
The predicted scores of adversarial examples can be 10 to 20 points (total 100) higher than the original scores of clean images. The Spearman's Rank-Order Correlation Coefficient (SROCC) between the predicted scores of adversarial examples and the original scores of clean images can be reduced to negative values. The Mean Squared Error (MSE) between the predicted scores of adversarial examples and the original scores of clean images can be increased significantly.
Quotes
"Current adversarial attacks, however, focus on perturbing predicted scores of individual images, neglecting the crucial aspect of inter-score correlation relationships within an entire image set." "To comprehensively explore the robustness of NR-IQA models, we introduce a new framework of correlation-error-based attacks that perturb both the correlation within an image set and score changes on individual images." "Experimental results demonstrate that our SMA method not only significantly disrupts the SROCC to negative values but also maintains a considerable change in the scores of individual images."

Deeper Inquiries

How can the proposed correlation-error-based attack framework be extended to other regression tasks beyond image quality assessment

The correlation-error-based attack framework proposed in the study can be extended to other regression tasks beyond image quality assessment by adapting the optimization objectives and constraints to suit the specific requirements of the new task. The key idea is to focus on perturbing both the correlation within the dataset and the score changes on individual samples. This approach can be applied to tasks such as sentiment analysis, financial forecasting, medical diagnosis, and more. By formulating the optimization problem with relevant correlation-based and error-based metrics specific to the new task, the framework can be tailored to attack models in various regression domains.

What are the potential countermeasures or defense strategies that can be developed to improve the robustness of NR-IQA models against such correlation-error-based attacks

To improve the robustness of NR-IQA models against correlation-error-based attacks, several countermeasures and defense strategies can be developed. Adversarial Training: Incorporate adversarial training during the model training phase to expose the model to adversarial examples and enhance its resilience against such attacks. Regularization Techniques: Implement regularization techniques such as L1 or L2 regularization to prevent overfitting and increase the model's generalization capabilities. Ensemble Methods: Utilize ensemble methods by combining multiple models to make predictions, which can help in detecting and mitigating the impact of adversarial attacks. Input Preprocessing: Apply input preprocessing techniques such as data augmentation, noise injection, or feature scaling to make the model more robust to perturbations in the input data. Robust Optimization: Optimize the model parameters with robust optimization techniques that consider worst-case scenarios and adversarial perturbations during training. Anomaly Detection: Implement anomaly detection mechanisms to identify and flag suspicious or adversarial samples before they impact the model's predictions. By implementing a combination of these strategies, NR-IQA models can enhance their resistance to correlation-error-based attacks and improve their overall robustness in real-world scenarios.

How can the insights gained from this study on the vulnerability of NR-IQA models be applied to enhance the design and development of more secure and reliable image processing algorithms in real-world applications

The insights gained from the study on the vulnerability of NR-IQA models can be applied to enhance the design and development of more secure and reliable image processing algorithms in real-world applications in the following ways: Improved Model Evaluation: Incorporate robustness testing and evaluation metrics that consider both error-based and correlation-based aspects to ensure the model's performance is consistent and reliable across different scenarios. Enhanced Model Training: Integrate adversarial training and regularization techniques during the model training phase to improve the model's resilience against adversarial attacks and perturbations. Dynamic Defense Mechanisms: Develop dynamic defense mechanisms that can adapt to evolving attack strategies and protect the model from new forms of adversarial perturbations. Continuous Monitoring: Implement continuous monitoring and anomaly detection systems to detect and mitigate potential attacks in real-time, ensuring the model's security and reliability. Collaborative Research: Foster collaboration and knowledge-sharing within the research community to collectively address the challenges of adversarial attacks and develop standardized defense strategies for image processing algorithms. By applying these strategies and leveraging the insights gained from the study, the design and development of image processing algorithms can be enhanced to be more secure, reliable, and resilient in the face of adversarial threats.
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