Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks (1707.02476v1)

Abstract: Deep neural networks (DNNs) have excellent representative power and are state of the art classifiers on many tasks. However, they often do not capture their own uncertainties well making them less robust in the real world as they overconfidently extrapolate and do not notice domain shift. Gaussian processes (GPs) with RBF kernels on the other hand have better calibrated uncertainties and do not overconfidently extrapolate far from data in their training set. However, GPs have poor representational power and do not perform as well as DNNs on complex domains. In this paper we show that GP hybrid deep networks, GPDNNs, (GPs on top of DNNs and trained end-to-end) inherit the nice properties of both GPs and DNNs and are much more robust to adversarial examples. When extrapolating to adversarial examples and testing in domain shift settings, GPDNNs frequently output high entropy class probabilities corresponding to essentially "don't know". GPDNNs are therefore promising as deep architectures that know when they don't know.

• The paper presents empirical evidence that GP-DNN hybrid models demonstrate superior performance when subjected to adversarial attacks compared to conventional DNN architectures.
• In-depth analysis shows that hybrid models offer improved uncertainty quantification, critical for distinguishing between genuine data points and adversarial samples.
• The authors find that GP-DNN hybrids exhibit more resistance to transferred attacks than their pure DNN counterparts.

Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks

This paper, authored by John Bradshaw, Alexander G. de G. Matthews, and Zoubin Ghahramani, investigates the robustness of hybrid models combining Gaussian Processes (GP) with deep neural networks (DNNs) under adversarial settings. The research provides a comprehensive analysis of adversarial examples, uncertainty quantification, and transferability aspects in Gaussian Process Hybrid Deep Networks.

The authors explore a topic of significant interest: the resilience of hybrid Gaussian Process-DNN frameworks against perturbations known as adversarial attacks. These adversarial examples are inputs designed to deceive neural networks, resulting in erroneous outputs. The paper meticulously examines how integrating Gaussian Processes with DNNs can potentially enhance the robustness of ML models by leveraging the GP’s capability to provide uncertainty estimations.

Several key findings emerge from this paper:

1. Adversarial Robustness: The paper presents empirical evidence that GP-DNN hybrid models demonstrate superior performance when subjected to adversarial attacks compared to conventional DNN architectures. This robustness can be attributed to the hybrid models' ability to model uncertainty effectively and potentially reject adversarial inputs with high uncertainty.
2. Uncertainty Estimation: In-depth analysis shows that hybrid models offer improved uncertainty quantification, which is critical for distinguishing between genuine data points and adversarial samples. The incorporation of Gaussian Processes ensures a more accurate assessment of uncertainty leading to better calibration and reliability in model predictions.
3. Transferability Testing: The authors address the transferability of adversarial attacks between distinct model architectures, an essential consideration for security in deployment scenarios. The paper finds that GP-DNN hybrids exhibit more resistance to transferred attacks than their pure DNN counterparts, underscoring the potential of these hybrid models in secure applications.

The practical implications of this research are substantial and diverse. In security-sensitive applications, employing GP-DNN hybrids could mitigate risks associated with adversarial machine learning attacks. Moreover, the enhanced uncertainty quantification provided by these models holds promise for fields where error margins and confidence levels are critical, such as autonomous vehicles and medical diagnostics.

Theoretically, the paper contributes to the ongoing discourse on hybrid ML architectures by offering evidence of their advantages in specific adversarial and uncertainty contexts. It opens avenues for further exploration into how hybrid models can be optimized and tailored for particular applications while retaining robustness to adversarial threats.

Future developments in AI may see the proliferation of hybrid models combining Gaussian Processes and DNNs, driven by an increasing demand for robust solutions against adversarial manipulation. As ML applications expand into more critical domains, the ability to quantify uncertainty and resist adversarial perturbations will only grow in importance.

In conclusion, this paper provides substantive insights into the potential advantages of Gaussian Process Hybrid Deep Networks, emphasizing the need for continued investigation into their applications and enhancements in adversarial settings.