Summary of Diffgan: a Test Generation Approach For Differential Testing Of Deep Neural Networks, by Zohreh Aghababaeyan et al.
DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks
by Zohreh Aghababaeyan, Manel Abdellatif, Lionel Briand, Ramesh S
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Deep Neural Networks (DNNs) are widely used in various applications. However, ensuring their reliability remains a significant challenge. Traditional accuracy-based evaluations often fail to capture behavioral differences between models, especially with limited test datasets. This makes it difficult to select or combine models effectively. To address these challenges, researchers propose DiffGAN, a black-box test image generation approach for differential testing of DNN models. DiffGAN leverages Generative Adversarial Network (GAN) and Non-dominated Sorting Genetic Algorithm II to generate diverse and valid triggering inputs that reveal behavioral discrepancies between models. Two custom fitness functions focus on diversity and divergence to guide the exploration of the GAN input space and identify discrepancies between models’ outputs. By strategically searching this space, DiffGAN generates inputs with specific features that trigger differences in model behavior. The proposed approach is black-box, making it applicable in more situations. Evaluation results show that DiffGAN significantly outperforms a state-of-the-art baseline, generating four times more triggering inputs, with greater diversity and validity, within the same budget. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have many computer models that can do similar things, like recognize images or predict numbers. But how do you know which one is the best? Traditional methods often don’t work well because they only look at how accurate each model is. What if two models are both very good, but behave differently in certain situations? To solve this problem, researchers developed a new approach called DiffGAN. It’s like a super-powerful image generator that can create special images to test which model behaves best. By using this method, you can pick the best model or combine them to get even better results. In tests, DiffGAN worked much better than other methods and could find more differences between models. |
Keywords
» Artificial intelligence » Gan » Generative adversarial network » Image generation
