Adversarial attacks and defenses in explainable artificial intelligence: A survey (2306.06123v3)

Abstract: Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging and trusting statistical and deep learning models, as well as interpreting their predictions. However, recent advances in adversarial machine learning (AdvML) highlight the limitations and vulnerabilities of state-of-the-art explanation methods, putting their security and trustworthiness into question. The possibility of manipulating, fooling or fairwashing evidence of the model's reasoning has detrimental consequences when applied in high-stakes decision-making and knowledge discovery. This survey provides a comprehensive overview of research concerning adversarial attacks on explanations of machine learning models, as well as fairness metrics. We introduce a unified notation and taxonomy of methods facilitating a common ground for researchers and practitioners from the intersecting research fields of AdvML and XAI. We discuss how to defend against attacks and design robust interpretation methods. We contribute a list of existing insecurities in XAI and outline the emerging research directions in adversarial XAI (AdvXAI). Future work should address improving explanation methods and evaluation protocols to take into account the reported safety issues.

• The paper introduces a unified taxonomy to bridge adversarial machine learning and XAI, enhancing clarity in research and applications.
• It reviews diverse adversarial attack methods, detailing how techniques like LIME, SHAP, and Grad-CAM can be manipulated to mislead model explanations.
• It evaluates defense strategies such as model regularization and explanation aggregation, stressing the need for advanced benchmarks and evolving countermeasures.

Overview of "Adversarial attacks and defenses in explainable artificial intelligence: A survey"

The paper, "Adversarial attacks and defenses in explainable artificial intelligence: A survey," by Hubert Baniecki and Przemyslaw Biecek, presents a meticulous survey of research concerning adversarial interactions with explainable artificial intelligence (XAI) systems. The paper highlights the growing significance of understanding vulnerabilities within XAI methods, particularly in the context of adversarial machine learning (AdvML). The central thesis is that XAI, despite its transformative impact on model transparency, faces significant challenges due to adversarial threats which can manipulate, misrepresent, or 'fairwash' evidence of model reasoning, potentially misguiding stakeholders engaged in high-stakes decision-making.

Key Contributions and Findings

1. Unified Notation and Taxonomy: A significant contribution of the paper is the introduction of a unified notation and taxonomy to facilitate a common understanding among researchers across the fields of AdvML and XAI. This standardization aims to streamline future research and practical applications, enabling better communication and understanding of adversarial interactions within XAI.
2. Adversarial Attack Mechanisms: The paper systematically reviews multiple methodologies by which adversarial attacks compromise XAI methods. The survey identifies key adversarial strategies, such as data poisoning, model manipulation, backdoor attacks, and adversarial examples, illustrating how these can distort or manipulate the evidence provided by various explanation methods like LIME, SHAP, and Grad-CAM.
3. Evaluation of Defense Mechanisms: The researchers evaluate existing defense strategies that aim to enhance the robustness of XAI systems against adversarial threats. These defenses include model regularization, explanation aggregation, and locality-preserving sampling techniques, among others. The paper suggests that while some defenses have shown promise, the arms race between attackers and defenders necessitates a continuous evolution of mitigation techniques.
4. Implications and Future Directions: A critical outcome of the survey is the identification of gaps and future research trajectories. Notably, the paper emphasizes the need for advancing defense mechanisms, particularly against unaddressed attack vectors targeting global explanations and fairness metrics. The authors advocate for comprehensive benchmark datasets and standardized evaluation metrics to better assess the effectiveness of defense mechanisms.

Implications for AI and Speculation on Developments

The insights from this paper have profound implications for the development and deployment of AI systems. As machine learning models are increasingly integrated into sensitive domains such as healthcare, finance, and autonomous vehicles, the robustness of explanations provided by XAI methods becomes crucial. This survey not only underscores the urgency of advancing defenses in XAI but also highlights the ethical considerations pertaining to transparency, accountability, and fairness in AI systems.

The intersection of adversarial machine learning and XAI holds potential for significant research developments. Given the increasing sophistication of adversarial attacks, future AI systems must be designed with inherent robustness and adaptive defense mechanisms. This implies a shift towards more interdisciplinary research, integrating insights from cybersecurity, human-computer interaction, and cognitive sciences to create trustworthy AI systems that can withstand adversarial influences.

In conclusion, "Adversarial attacks and defenses in explainable artificial intelligence: A survey" provides a comprehensive landscape of the existing challenges and opportunities within the field of AdvXAI. As this field evolves, continuous collaboration between researchers and practitioners will be essential to fortify AI systems against adversarial threats, ensuring safer and more transparent AI applications in society.