Causal Perception Inspired Representation Learning for Improving Robustness of Image Quality Assessment Models

Core Concepts

Causal perception inspired representation learning can enhance the robustness of image quality assessment models against adversarial attacks by extracting causally sufficient and necessary features.

Abstract

The paper proposes a Causal Perception inspired Representation Learning (CPRL) approach to build a trustworthy image quality assessment (IQA) model that is robust to adversarial attacks.
The key insights are:

IQA models that rely solely on statistical associations are vulnerable to adversarial perturbations, as they can be fooled by changes in non-perceptual features that do not affect human perception.
The authors model the IQA task using a causal framework, where each image is composed of a Causal Perception Representation (CPR) and a Non-Causal Perception Representation (N-CPR). CPR is the causation of the subjective quality label and is invariant to adversarial perturbations, while N-CPR presents spurious associations that can be easily manipulated.
To extract the CPR, the authors develop a soft ranking-based channel-wise activation function to mediate the causally sufficient and necessary deep features, and optimize this using a minimax game based on the Probability of Necessity and Sufficiency (PNS) risk.
Experiments on four benchmark IQA datasets show that the proposed CPRL method outperforms state-of-the-art adversarial defense methods in terms of both accuracy and robustness to adversarial attacks.

Stats

The paper reports the following key metrics:

Spearman's Rank Order Correlation Coefficient (SROCC) and Pearson's Linear Correlation Coefficient (PLCC) on four IQA datasets (LIVE, VCL, LIVEC, KONIQ) under clean and adversarial (FGSM, PGD) conditions.
Mean Squared Error (MSE) on the same datasets and conditions.

Quotes

"IQA models that only depend on statistical associations are inadequate and unreliable, our method uses a causal intervention to break the false correlations and find the true cause of image quality."
"We want to maximize PNS during training to make the feature causally relevant."

Key Insights Distilled From

Causal Perception Inspired Representation Learning for Trustworthy Image Quality Assessment

by Lei Wang,Des... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19567.pdf

Deeper Inquiries

How can the proposed CPRL approach be extended to other computer vision tasks beyond IQA, such as image classification or object detection, to improve their robustness?

The CPRL approach can be extended to other computer vision tasks by incorporating causal perception inspired representation learning into the model architecture. For image classification tasks, the CPRL framework can be used to identify causal features that are essential for accurate classification while being robust to adversarial perturbations. By optimizing the model based on causally sufficient and necessary features, the model can learn to focus on the most relevant information for classification, leading to improved robustness.
In the case of object detection, CPRL can help in identifying causal relationships between object features and their presence in an image. By emphasizing features that are causally related to the presence of objects rather than spurious correlations, the object detection model can become more reliable in detecting objects even in the presence of adversarial attacks.
Overall, by integrating CPRL principles into the design and training of computer vision models for tasks beyond IQA, it is possible to enhance their robustness and reliability in real-world applications.

What are the potential limitations of the causal intervention approach, and how can they be addressed in future work?

One potential limitation of the causal intervention approach is the complexity and computational overhead involved in optimizing the model based on causally sufficient and necessary features. This can lead to longer training times and increased resource requirements, making the approach less practical for large-scale applications. To address this limitation, future work can focus on developing more efficient optimization algorithms and techniques that can streamline the process of identifying and leveraging causal features in the model.
Another limitation is the interpretability of the causal relationships identified by the model. While CPRL aims to extract causally relevant features, the exact causal mechanisms underlying these features may not always be clear. Future research can explore ways to enhance the interpretability of causal interventions and provide more insights into how the model makes decisions based on causal relationships.
Additionally, the generalization of the causal intervention approach to different types of data and tasks may pose a challenge. Ensuring that the approach is effective across various domains and datasets requires careful consideration and validation. Future work can focus on testing the robustness and effectiveness of causal interventions in diverse settings to address this limitation.

Can the CPRL framework be combined with other adversarial defense techniques, such as adversarial training or input transformation, to further enhance the robustness of IQA models?

Yes, the CPRL framework can be effectively combined with other adversarial defense techniques to enhance the robustness of IQA models. By integrating CPRL principles with adversarial training, the model can learn to identify and prioritize causally relevant features while also being trained to resist adversarial attacks. This combined approach can lead to a more robust IQA model that is both accurate and resilient to adversarial perturbations.
Incorporating input transformation techniques along with CPRL can further enhance the model's robustness. By preprocessing the input data to emphasize causal features and reduce the impact of non-causal factors, the model can become more resistant to adversarial attacks. Input transformation methods such as data augmentation or noise injection can complement the CPRL framework by providing additional layers of defense against adversarial perturbations.
Overall, by combining the CPRL framework with other adversarial defense techniques, IQA models can achieve a higher level of robustness and reliability in challenging real-world scenarios.