SINVAD: Search-based Image Space Navigation for DNN Image Classifier Test Input Generation (2005.09296v1)

Abstract: The testing of Deep Neural Networks (DNNs) has become increasingly important as DNNs are widely adopted by safety critical systems. While many test adequacy criteria have been suggested, automated test input generation for many types of DNNs remains a challenge because the raw input space is too large to randomly sample or to navigate and search for plausible inputs. Consequently, current testing techniques for DNNs depend on small local perturbations to existing inputs, based on the metamorphic testing principle. We propose new ways to search not over the entire image space, but rather over a plausible input space that resembles the true training distribution. This space is constructed using Variational Autoencoders (VAEs), and navigated through their latent vector space. We show that this space helps efficiently produce test inputs that can reveal information about the robustness of DNNs when dealing with realistic tests, opening the field to meaningful exploration through the space of highly structured images.

Overview of the SINVAD Approach to DNN Image Classifier Testing

The paper "SINVAD: Search-based Image Space Navigation for DNN Image Classifier Test Input Generation" explores the challenges associated with testing Deep Neural Networks (DNNs), particularly when these systems are employed in safety-critical domains such as autonomous driving and medical imaging. With increasing reliance on DNNs, the need for robust and comprehensive testing methodologies has become imperative, especially given the susceptibility of these systems to issues such as adversarial attacks. The work presented here introduces SINVAD, a novel approach utilizing Variational Autoencoders (VAEs) to navigate the space of plausible inputs to generate effective test cases for image classifiers.

Challenge in DNN Testing

DNN testing faces significant challenges due to the high dimensionality of input spaces and the difficulty of generating meaningful and diverse test inputs. Traditional approaches often rely on metamorphic testing principles, which focus on small perturbations to existing inputs. However, such methods can be limiting as they do not fully explore the broader space of valid inputs that a DNN might encounter in real-world scenarios.

SINVAD Methodology

SINVAD addresses these challenges by introducing a search-based method that focuses on the plausible input space, derived from the training data distribution rather than the entire input space. By leveraging VAEs, the approach generates and optimizes inputs within a latent space that corresponds to realistic images.

VAEs are powerful generative models that encode inputs into a lower-dimensional latent space, allowing for the efficient search and generation of novel, yet semantically plausible images. This method contrasts with traditional random search techniques, which often generate uninterpretable noise.

Experimental Evaluation and Results

The authors demonstrate the effectiveness of SINVAD through several experiments:

1. Generation of Realistic Images: By comparing the outcomes of search-based approaches at the raw pixel level versus the VAE-constructed latent space, the paper shows that the latter produces more realistic and structured images. This was validated through qualitative assessments and the analysis of where generated images reside in the activation trace space.
2. Indecisive Image Detection: SINVAD successfully generates images near DNN decision boundaries, as evidenced by increased prediction variability under dropout conditions. This capability is critical for the identification of edge cases where DNNs may fail to generalize.
3. Cross-Model Testing: The approach is used to perform differential testing between models, uncovering discrepancies in their behavior. This can inform the development of more robust model ensembles and highlight areas of concern.
4. Insight into DNN Vulnerabilities: The method's ability to adjust to a targeted class escape criterion further demonstrates its utility in pinpointing weak spots in classifier performance, offering insights into potential areas where models might need more substantial training data or adjustments.

Implications and Future Directions

The paper provides meaningful insights on testing neural networks by shifting focus from mere perturbation-based methods to exploring structured and semantically significant input spaces. This work could lead to significant advancements in reliable DNN deployment in safety-critical areas. Future research could extend this approach to other types of data modalities and further optimize the latent search methodology to improve the detection of adversarial vulnerabilities. Additionally, the potential for cross-architecture analysis may help unify testing strategies across varied DNN designs, thereby enhancing overall system robustness.