A Comprehensive Survey on Enhancing Transferability of Adversarial Examples Across Deep Neural Networks

Conceitos Básicos

Adversarial examples, which are imperceptible perturbations that can fool deep neural networks, exhibit the intriguing property of transferability - where perturbations crafted for one model can also deceive other models with different architectures. This survey explores the latest research on enhancing the transferability of adversarial examples across deep neural networks.

Resumo

This survey provides a comprehensive overview of the current research on enhancing the transferability of adversarial examples across deep neural networks. It first introduces the terminology, mathematical notations, and the formulation and evaluation of adversarial transferability.
The main body of the survey is divided into three sections:

Optimization-Based Transferable Attacks:

Data Augmentation-Based Methods: These methods apply various input transformations, such as random resizing, padding, and scaling, to increase the diversity of the inputs and improve transferability.
Optimization Technique-Based Methods: These methods focus on improving the optimization process itself, incorporating techniques like momentum, Nesterov acceleration, and variance reduction to enhance the transferability of the generated adversarial perturbations.
Loss Objective-Based Methods: These methods explore alternative loss functions, such as normalized cross-entropy, Poincaré distance metric, and triplet loss, to better guide the optimization towards generating transferable adversarial examples.
Model Component-Based Methods: These methods leverage the intermediate features and representations of the surrogate model to generate adversarial perturbations that are more transferable across different architectures.

Generation-Based Transferable Attacks:

Unconditional Generation: These methods use generative models, such as GANs and VAEs, to directly synthesize transferable adversarial examples without relying on a specific surrogate model.
Class-conditional Generation: These methods leverage class-conditional generative models to generate adversarial examples that are tailored to specific target classes, aiming to improve the transferability of targeted attacks.

Adversarial Transferability Beyond Image Classification:

The survey also discusses the research on adversarial transferability in other computer vision tasks, such as image retrieval, object detection, and segmentation, as well as in natural language processing tasks.

Finally, the survey outlines the current challenges and future research opportunities in the field of adversarial transferability, highlighting the importance of understanding and addressing this vulnerability to ensure the robustness and reliability of deep neural networks in real-world applications.

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Principais Insights Extraídos De

A Survey on Transferability of Adversarial Examples across Deep Neural Networks

by Jindong Gu,X... às arxiv.org 05-03-2024

https://arxiv.org/pdf/2310.17626.pdf

Perguntas Mais Profundas

How can the insights from adversarial transferability research be applied to improve the robustness of deep neural networks in safety-critical applications

The insights gained from adversarial transferability research can significantly contribute to enhancing the robustness of deep neural networks in safety-critical applications. By understanding how adversarial examples can deceive different models, even with varying architectures, researchers can develop more resilient models that are less susceptible to such attacks. This knowledge can be applied in safety-critical domains like autonomous driving systems, medical image analysis, and cybersecurity to fortify the models against potential adversarial threats.
One practical application of this research is in improving the security of autonomous vehicles. By studying how adversarial examples can transfer between different neural networks, developers can create more robust perception systems that are less likely to be misled by malicious inputs. This can help ensure the safety of autonomous vehicles by reducing the risk of attacks that could compromise their decision-making processes.
Furthermore, in medical image analysis, understanding adversarial transferability can lead to the development of more secure diagnostic systems. By incorporating transferability-enhancing techniques into the design of deep learning models used for medical imaging, researchers can mitigate the risk of adversarial attacks that could potentially alter diagnostic results and jeopardize patient safety.
Overall, leveraging the findings from adversarial transferability research can enable the creation of deep neural networks that are more resilient and reliable in safety-critical applications, ultimately enhancing the overall security and trustworthiness of these systems.

What are the potential limitations or drawbacks of the current transferability-enhancing methods, and how can they be addressed in future research

While current transferability-enhancing methods have shown promising results in improving the robustness of deep neural networks, there are still some limitations and drawbacks that need to be addressed in future research. Some potential limitations include:

Limited Generalization: Some transferability-enhancing methods may focus on specific architectures or tasks, limiting their generalizability across different models and domains. Future research should aim to develop more universal techniques that can be applied to a wide range of neural networks and applications.

Computational Complexity: Certain methods, such as those involving generative models or complex optimization techniques, may be computationally intensive and time-consuming. This can hinder their practicality in real-time applications or resource-constrained environments. Future research could explore more efficient algorithms that strike a balance between effectiveness and computational cost.

Adversarial Robustness: While transferability-enhancing methods aim to improve the robustness of neural networks, there is still a need to ensure that these models are resilient to a wide range of adversarial attacks. Future research should focus on developing comprehensive defense mechanisms that can withstand sophisticated adversarial threats.

To address these limitations, future research in adversarial transferability could focus on developing more scalable and versatile techniques, exploring novel approaches that prioritize both effectiveness and efficiency, and integrating robust defense strategies to enhance the overall security of deep neural networks in various applications.

Beyond computer vision and natural language processing, are there other domains where the study of adversarial transferability could yield valuable insights

The study of adversarial transferability extends beyond computer vision and natural language processing, offering valuable insights in various domains where machine learning models are utilized. Some potential domains where the exploration of adversarial transferability could yield valuable insights include:

Cybersecurity: Adversarial transferability research can be applied to enhance the security of cybersecurity systems, such as intrusion detection systems and malware detection. By understanding how adversarial examples can propagate across different models, cybersecurity professionals can develop more robust defense mechanisms to detect and mitigate cyber threats effectively.

Financial Services: In the financial sector, the study of adversarial transferability can help improve fraud detection systems and risk assessment models. By investigating how adversarial attacks can impact the predictions of financial models, researchers can enhance the resilience of these systems against fraudulent activities and malicious manipulations.

Healthcare: Adversarial transferability research can also be valuable in healthcare applications, such as patient diagnosis and treatment planning. By analyzing how adversarial examples can influence medical AI systems, healthcare providers can ensure the accuracy and reliability of AI-driven healthcare solutions, ultimately improving patient outcomes and safety.

By expanding the study of adversarial transferability to these diverse domains, researchers can uncover new insights and develop innovative strategies to enhance the robustness and security of machine learning models across various industries and applications.