Deep neural networks (DNNs) have successfully classified EEG-based brain-computer interface (BCI) systems. However, recent studies have found that well-designed input samples, known as adversarial examples, can easily fool well-performed deep neural networks model with minor perturbations undetectable by a human. This paper proposes an efficient generative model named generative perturbation network (GPN), which can generate universal adversarial examples with the same architecture for non-targeted and targeted attacks. Furthermore, the proposed model can be efficiently extended to conditionally or simultaneously generate perturbations for various targets and victim models. Our experimental evaluation demonstrates that perturbations generated by the proposed model outperform previous approaches for crafting signal-agnostic perturbations. We demonstrate that the extended network for signal-specific methods also significantly reduces generation time while performing similarly. The transferability across classification networks of the proposed method is superior to the other methods, which shows our perturbations' high level of generality. Our code is available for download on https://github.com/AIRLABkhu/Generative-Perturbation-Networks .

The susceptibility of deep neural networks (DNNs) to adversarial examples has prompted an increase in the deployment of adversarial attacks. Image-agnostic universal adversarial perturbations (UAPs) are much more threatening, but many limitations exist to implementing UAPs in real-world scenarios where only binary decisions are returned. In this research, we propose D-BADGE, a novel method to craft universal adversarial perturbations for executing decision- To primarily optimize perturbation by focusing on decisions, we consider the direction of these updates as the primary factor and the magnitude of updates as the secondary factor. First, we employ Hamming loss that measures the distance from distributions of ground truth and accumulating decisions in batches to determine the magnitude of the gradient. This magnitude is applied in the direction of the revised simultaneous perturbation stochastic approximation (SPSA) to update the perturbation. This simple yet efficient decision-based method functions similarly to a score-based attack, enabling the generation of UAPs in real-world scenarios, and can be easily extended to targeted attacks. Experimental validation across multiple victim models demonstrates that the D-BADGE outperforms existing attack methods, even image-specific and score-based attacks. In particular, our proposed method shows a superior attack success rate with less training time. The research also shows that D-BADGE can successfully deceive unseen victim models and accurately target specific classes.

Solubility measurements are essential in various research and industrial fields. With the automation of processes, the importance of automatic and real-time solubility measurements has increased. Although end-to-end learning methods are commonly used for classification tasks, the use of handcrafted features is still important for specific tasks with the limited labeled images of solutions used in industrial settings. In this study, we propose a method that uses computer vision algorithms to extract nine handcrafted features from images and train a DNN-based classifier to automatically classify solutions based on their dissolution states. To validate the proposed method, a dataset was constructed using various solution images ranging from undissolved solutes in the form of fine particles to those completely covering the solution. Using the proposed method, the solubility status can be automatically screened in real time by using a display and camera on a tablet or mobile phone. Therefore, by combining an automatic solubility changing system with the proposed method, a fully automated process could be achieved without human intervention.

The susceptibility of deep neural networks (DNNs) to adversarial examples has prompted an increase in the deployment of adversarial attacks. Image-agnostic universal adversarial perturbations (UAPs) are much more threatening, but many limitations exist to implementing UAPs in real-world scenarios where only binary decisions are returned. In this research, we propose Decision-BADGE, a novel method to craft universal adversarial perturbations for executing decision-based black-box attacks. To optimize perturbation with decisions, we addressed two challenges, namely the magnitude and the direction of the gradient. First, we use batch loss, differences from distributions of ground truth, and accumulating decisions in batches to determine the magnitude of the gradient. This magnitude is applied in the direction of the revised simultaneous perturbation stochastic approximation (SPSA) to update the perturbation. This simple yet efficient method can be easily extended to score-based attacks as well as targeted attacks. Experimental validation across multiple victim models demonstrates that the Decision-BADGE outperforms existing attack methods, even image-specific and score-based attacks. In particular, our proposed method shows a superior success rate with less training time. The research also shows that Decision-BADGE can successfully deceive unseen victim models and accurately target specific classes.

Deep learning has advanced vision tasks such as classification, segmentation, and detection. However, in real-world scenarios, models often encounter domains that differ from the ones seen during training, which can lead to substantial performance degradation. To mitigate the effects of distribution shifts, domain generalization (DG) aims to enable models to generalize effectively to unseen target domains. Recent DG approaches use generative models like diffusion models to augment data with text prompts. However, these methods rely on domain-specific textual inputs and costly fine-tuning, limiting their scalability. We propose TRIDENT, a framework that overcomes these limitations by eliminating the need for text prompts and leveraging the linear structure of CLIP embeddings. TRIDENT decomposes image embeddings into three components—Domain, Class, and Attribute—enabling precise control over semantic content. By reassembling each embedding component, we generate semantically valid and structurally coherent synthetic samples across domains. This allows efficient and diverse data synthesis without retraining diffusion models. TRIDENT operates through lightweight embedding-space manipulation, significantly reducing computational overhead. Extensive experiments on standard DG benchmarks (e.g., PACS, VLCS, and OfficeHome) demonstrate that TRIDENT achieves competitive or superior performance to existing approaches. Furthermore, qualitative evaluations and comprehensive analyses confirm that TRIDENT not only enables efficient and diverse data synthesis, but also demonstrates the effectiveness of the proposed decomposition strategy.

Deep Neural Networks (DNNs) have become pivotal in various fields, especially in computer vision, outperforming previous methodologies. A critical challenge in their deployment is the bias inherent in data across different domains, such as image style and environmental conditions, leading to domain gaps. This necessitates techniques for learning general representations from biased training data, known as domain generalization. This paper presents Attend to eXpert Prompts (A2XP), a novel approach for domain generalization that preserves the privacy and integrity of the network architecture. A2XP consists of two phases: Expert Adaptation and Domain Generalization. In the first phase, prompts for each source domain are optimized to guide the model towards the optimal direction. In the second phase, two embedder networks are trained to effectively amalgamate these expert prompts, aiming for an optimal output. Our extensive experiments demonstrate that A2XP achieves state-of-the-art results over existing non-private domain generalization methods. The experimental results validate that the proposed approach not only tackles the domain generalization challenge in DNNs but also offers a privacy-preserving, efficient solution to the broader field of computer vision.