arXiv:2505. 08814v3 Announce Type: replace-cross Abstract: Deep neural networks (DNNs) play a crucial role in the field of artificial intelligence, and their security-related testing has been a prominent research focus.
arXiv:2602. 13298v4 Announce Type: replace-cross Abstract: This paper presents a controlled comparative study of convolutional neural network (CNN) topology and image classification performance across the architectural families VGG, ResNet, and GoogLeNet, evaluated on CIFAR-10 under a unified training protocol.
arXiv:2608. 12144v1 Announce Type: cross Abstract: Over the past decade, many test adequacy metrics have been proposed for deep learning that characterize test dataset adequacy from different perspectives, e.
Over the past decade, deep neural networks (DNNs) have achieved remarkable success on complex machine-learning tasks, yet the theoretical foundations of their performance remain incomplete. From a statistical viewpoint, a natural question is: can DNNs attain feature-learning and prediction consistency comparable to that of classical models?
The paper reviews 50 image augmentation and generation techniques, categorizing them into ten groups, and conducts a large‑scale empirical study to assess their effectiveness as test generators for embedding‑based image retrieval systems. Using Amazon Titan and OpenCLIP embeddings, the authors evaluate the techniques across four dimensions—embedding‑space similarity, embedding uncertainty, semantic realism, and retrieval failure rate—on CIFAR‑10, ImageNet‑1K, and an industrial dataset. Results show that weather simulation and SaSPA yield the highest uncertainty and failure rates while maintaining realistic visuals, whereas GAN‑based methods produce low realism due to synthetic artifacts.
arXiv:2211. 14966v2 Announce Type: replace Abstract: Deep neural networks (DNNs) are highly vulnerable to adversarial attacks.
arXiv:2409. 10094v3 Announce Type: replace-cross Abstract: Out-of-Distribution (OoD) detection aims to justify whether a given sample is from the training distribution of the classifier-under-protection, i.
arXiv:2606. 08033v1 Announce Type: cross Abstract: Cracks are a critical indicator of building health, and early stage identification is fundamental to prevent harmful damages.
arXiv:2512. 10485v2 Announce Type: replace-cross Abstract: Vulnerability detection methods based on deep learning (DL) have shown strong performance on benchmark datasets, yet their real-world effectiveness remains underexplored.
The paper investigates Automatic Target Recognition (ATR) in Synthetic Aperture Sonar (SAS) imagery, comparing modern convolutional neural networks (CNNs) and transformer-based deep neural networks (DNNs). It examines how factors such as network size, architecture, pretraining methods, data augmentation, and regularization influence performance, aiming to identify the highest-performing model and provide a training roadmap for state‑of‑the‑art SAS‑ATR systems.
arXiv:2606. 04314v1 Announce Type: new Abstract: As neural networks are increasingly deployed in safety-critical domains, testing is essential to evaluate and improve their reliability.
arXiv:2607. 26580v1 Announce Type: cross Abstract: With the increase in the number of cases related to respiratory diseases, there is an urgent need to detect them early and diagnose them accurately.
arXiv:2602.01718v2 Announce Type: replace Abstract: Predicting generalization from quantities available before target-test evaluation remains a central challenge in deep learning. The systematic benc...