Bayesian Neural Networks' Robustness to Adversarial Attacks

Temel Kavramlar

Bayesian Neural Networks exhibit robustness to adversarial attacks due to the vanishing expected orthogonal gradient, providing protection against gradient-based attacks.

Özet

The content explores the robustness of Bayesian Neural Networks (BNNs) to adversarial attacks by analyzing their geometry and vulnerability. The study shows that BNNs can achieve zero expected orthogonal gradients, making them resilient to such attacks. Experimental results on various datasets support the theoretical findings.
The authors discuss the vulnerability of deep learning models to adversarial attacks and highlight the importance of developing robust models for safety-critical applications. They analyze the geometry of data manifolds and demonstrate how BNNs can resist both gradient-based and gradient-free adversarial attacks. The paper provides theoretical proofs and empirical evidence supporting the robustness of BNNs.
Key points include:

Vulnerability of deep learning models to adversarial attacks.
Analysis of data manifold geometry in relation to adversarial attacks.
Demonstration of BNNs' resistance to various types of adversarial attacks.
Theoretical proofs and empirical validation supporting BNNs' robustness.

İstatistikler

For each x ∈ M it holds that ⟨∇⊥x z(x)⟩p(z(x)|DN) = 0.
For any x ∈ M it holds that ⟨∇⊥x f(x, w)⟩p(f(x,w)|DN) = 0.

Alıntılar

"The interest behind infinitely-wide neural networks lies in their universal approximation capabilities."
"Empirical observations support increased adversarial robustness in Bayesian Neural Networks."

Önemli Bilgiler Şuradan Elde Edildi

On the Robustness of Bayesian Neural Networks to Adversarial Attacks

by Luca Bortolu... : arxiv.org 02-29-2024

https://arxiv.org/pdf/2207.06154.pdf

Daha Derin Sorular

How do varying architectures impact the robustness of Bayesian Neural Networks

Varying architectures have a significant impact on the robustness of Bayesian Neural Networks (BNNs). In the context provided, it is observed that wider networks tend to achieve lower orthogonal gradients, leading to increased robustness against adversarial attacks. The architecture of BNNs plays a crucial role in determining their ability to cancel out gradients in directions orthogonal to the data manifold. As demonstrated in the empirical results, wider networks exhibit decreased average and maximum orthogonal gradients compared to narrower architectures. This suggests that architectural choices such as network width can influence the model's resilience to adversarial perturbations. Additionally, varying hyperparameters like σ, which represents the standard deviation of the normal likelihood used in training, also impact the robustness of BNNs.

What are potential limitations or drawbacks of relying on Gaussian Processes for model robustness

While Gaussian Processes (GPs) offer certain advantages for enhancing model robustness against adversarial attacks, there are potential limitations and drawbacks associated with relying solely on them. One limitation is related to computational complexity and scalability issues when dealing with large datasets or high-dimensional input spaces. GPs require storing and manipulating covariance matrices that grow quadratically with dataset size, making them computationally expensive for big data applications. Moreover, GPs assume specific kernel functions that may not always capture complex patterns effectively or generalize well across different types of data distributions. Another drawback is their limited capacity for capturing non-linear relationships between variables without appropriate kernel engineering or feature transformations.

How might the findings on BNN resilience to adversarial attacks extend beyond neural network applications

The findings regarding Bayesian Neural Networks' resilience to adversarial attacks have broader implications beyond neural network applications. The concept of leveraging Bayesian inference principles and ensemble learning techniques for improving model robustness can be extended to various machine learning algorithms beyond just neural networks. By incorporating uncertainty estimates into models through probabilistic approaches like BNNs or GPs, other types of classifiers could potentially benefit from enhanced generalization performance and resistance against adversarial manipulations.