Test input generators (TIGs) are widely used to assess the robustness of Deep Learning (DL) image classifiers, yet they often produce invalid inputs that fall outside the semantic domain of the task, misleading quality assessment. While several automated validators have been proposed, there is a critical mismatch between automated and human validation criteria and, thus, automated validators are merely a proxy of domain validity, as perceived by human testers. We introduce DeepNaqqal, a supervised test input validator that learns validity directly from human-annotated labels using transfer learning on deep vision models. Our empirical study on automated validation of misclassification-inducing inputs compares DeepNaqqal against six state-of-the-art validators across three image classification tasks and multiple TIG families, using independent human assessment as ground truth. Our results show that DeepNaqqal consistently achieves the highest agreement with human judgments, while generalizing to unseen TIGs and remaining effective with substantially reduced labeled data.

GIFTbench is a modular framework for testing Deep Learning image classifiers that combines Generative AI with genetic algorithms. Its architecture integrates pretrained generative models with a user-friendly Gradio interface, enabling automated, reproducible, and interpretable robustness testing. Supporting VAE, GAN, and Diffusion models, GIFTbench generates test inputs by perturbing latent representations to expose misbehaviors of the classifier under test. By automating test input generation and reducing the need for manual coding, GIFTbench accelerates experimentation and facilitates comparative evaluation of both classifiers and generative models. Designed for researchers and practitioners, it enables reproducible assessment of image classifiers, while supporting studies on classifier vulnerabilities, mutation strategies, and the role of generative models in robustness testing.

Test Input Generators (TIGs) are crucial to assess the ability of Deep Learning (DL) image classifiers to provide correct predictions for inputs beyond their training and test sets. Recent advancements in Generative AI(GenAI) models have made them a powerful tool for creating and manipulating synthetic images, although these advancements also imply increased complexity and resource demands for training. In this work, we benchmark and combine different GenAI models with TIGs, assessing their effectiveness, efficiency, and quality of the generated test images, in terms of domain validity and label preservation. We conduct an empirical study involving three different GenAI architectures (VAEs, GANs, Diffusion Models), five classification tasks of increasing complexity, and 364 human evaluations. Our results show that simpler architectures, such as VAEs, are sufficient for less complex datasets like MNIST. However, when dealing with feature-rich datasets, such as ImageNet, more sophisticated architectures like Diffusion Models achieve superior performance by generating a higher number of valid, misclassification-inducing inputs.