The growing use of FPGAs in reconfigurable systems introducessecurity risks through malicious bitstreams that could cause denial-of-service (DoS), data leakage, or covert attacks. We investigated chip-level hardware malicious payload in embedded systems and proposed a supervised machine learning method to detect malicious bitstreams via static byte-level features. Our approach diverges from existing methods by analyzing bitstreams directly at the binary level, enabling real-time detection without requiring access to source code or netlists. Bitstreams were sourced from state-of-the-art (SOTA) benchmarks and re-engineered to target the Xilinx PYNQ-Z1 FPGA Development Board. Our dataset included 122 samples of benign and malicious configurations. The data were vectorized using byte frequency analysis, compressed using TSVD, and balanced using SMOTE to address class imbalance. The evaluated classifiers demonstrated that Random Forest achieved a macro F1-score of 0.97, underscoring the viability of real-time Trojan detection on resource-constrained systems. The final model was serialized and successfully deployed via PYNQ to enable integrated bitstream analysis.

This study introduces a novel data-centric approach to improve real-time surgical guidance using fiber-based fluorescence lifetime imaging (FLIm). A key aspect of the methodology is the accurate detection of the aiming beam, which is essential for localizing points used to map FLIm measurements onto the tissue region within the surgical field. The primary challenge arises from the complex and variable conditions encountered in the surgical environment, particularly in Transoral Robotic Surgery (TORS). Uneven illumination in the surgical field can cause reflections, reduce contrast, and results in inconsistent color representation, further complicating aiming beam detection. To overcome these challenges, an instance segmentation model was developed using a data-centric training strategy that improves accuracy by minimizing label noise and enhancing detection robustness. The model was evaluated on a dataset comprising 40 in vivo surgical videos, demonstrating a median detection rate of 85%. This performance was maintained when the model was integrated in a clinical system, achieving a similar detection rate of 85% during TORS procedures conducted in patients. The system's computational efficiency, measured at approximately 24 frames per second (FPS), was sufficient for real-time surgical guidance. This study enhances the reliability of FLIm-based aiming beam detection in complex surgical environments, advancing the feasibility of real-time, image-guided interventions for improved surgical precision

Regional discrimination is a persistent social issue in Vietnam. While existing research has explored hate speech in the Vietnamese language, the specific issue of regional discrimination remains under-addressed. Previous studies primarily focused on model development without considering practical system implementation. In this work, we propose a task called Detection of Regional Discriminatory Comments on Vietnamese Social Media, leveraging the power of machine learning and transfer learning models. We have built the ViRDC (Vietnamese Regional Discrimination Comments) dataset, which contains comments from social media platforms, providing a valuable resource for further research and development. Our approach integrates streaming capabilities to process real-time data from social media networks, ensuring the system's scalability and responsiveness. We developed the system on the Apache Spark framework to efficiently handle increasing data inputs during streaming. Our system offers a comprehensive solution for the real-time detection of regional discrimination in Vietnam.

In this paper, we present an integrated approach to real-time mosquito detection using our multiclass dataset (MosquitoFusion) containing 1204 diverse images and leverage cutting-edge technologies, specifically computer vision, to automate the identification of Mosquitoes, Swarms, and Breeding Sites. The pre-trained YOLOv8 model, trained on this dataset, achieved a mean Average Precision (mAP@50) of 57.1%, with precision at 73.4% and recall at 50.5%. The dataset and code are available at https://github.com/ Mosquito-borne diseases stand as a major global health threat due to the adaptability and resilience of mosquitoes. Roughly 700 million people are infected with mosquito-borne diseases every year.

Detection of small, undetermined moving objects or objects in an occluded environment with a cluttered background is the main problem of computer vision. This greatly affects the detection accuracy of deep learning models. To overcome these problems, we concentrate on deep learning models for real-time detection of cars and tanks in an occluded environment with a cluttered background employing SSD and YOLO algorithms and improved precision of detection and reduce problems faced by these models. The developed method makes the custom dataset and employs a preprocessing technique to clean the noisy dataset. For training the developed model we apply the data augmentation technique to balance and diversify the data. We fine-tuned, trained, and evaluated these models on the established dataset by applying these techniques and highlighting the results we got more accurately than without applying these techniques. The accuracy and frame per second of the SSD-Mobilenet v2 model are higher than YOLO V3 and YOLO V4. Furthermore, by employing various techniques like data enhancement, noise reduction, parameter optimization, and model fusion we improve the effectiveness of detection and recognition. We further added a counting algorithm, and target attributes experimental comparison, and made a graphical user interface system for the developed model with features of object counting, alerts, status, resolution, and frame per second. Subsequently, to justify the importance of the developed method analysis of YOLO V3, V4, and SSD were incorporated. Which resulted in the overall completion of the proposed method.

There are growing implications surrounding generative AI in the speech domain that enable voice cloning and real-time voice conversion from one individual to another. This technology poses a significant ethical threat and could lead to breaches of privacy and misrepresentation, thus there is an urgent need for real-time detection of AI-generated speech for DeepFake Voice Conversion. To address the above emerging issues, the DEEP-VOICE dataset is generated in this study, comprised of real human speech from eight well-known figures and their speech converted to one another using Retrieval-based Voice Conversion. Presenting as a binary classification problem of whether the speech is real or AI-generated, statistical analysis of temporal audio features through t-testing reveals that there are significantly different distributions. Hyperparameter optimisation is implemented for machine learning models to identify the source of speech. Following the training of 208 individual machine learning models over 10-fold cross validation, it is found that the Extreme Gradient Boosting model can achieve an average classification accuracy of 99.3% and can classify speech in real-time, at around 0.004 milliseconds given one second of speech. All data generated for this study is released publicly for future research on AI speech detection.

In this paper a deep learning architecture is presented that can, in real time, detect the 2D locations of certain landmarks of physical tools, such as a hammer or screwdriver. To avoid the labor of manual labeling, the network is trained on synthetically generated data. Training computer vision models on computer generated images, while still achieving good accuracy on real images, is a challenge due to the difference in domain. The proposed method uses an advanced rendering method in combination with transfer learning and an intermediate supervision architecture to address this problem. It is shown that the model presented in this paper, named Intermediate Heatmap Model (IHM), generalizes to real images when trained on synthetic data. To avoid the need for an exact textured 3D model of the tool in question, it is shown that the model will generalize to an unseen tool when trained on a set of different 3D models of the same type of tool. IHM is compared to two existing approaches to keypoint detection and it is shown that it outperforms those at detecting tool landmarks, trained on synthetic data.

To control boll weevil (Anthonomus grandis L.) pest re-infestation in cotton fields, the current practices of volunteer cotton (VC) (Gossypium hirsutum L.) plant detection in fields of rotation crops like corn (Zea mays L.) and sorghum (Sorghum bicolor L.) involve manual field scouting at the edges of fields. This leads to many VC plants growing in the middle of fields remain undetected that continue to grow side by side along with corn and sorghum. When they reach pinhead squaring stage (5-6 leaves), they can serve as hosts for the boll weevil pests. Therefore, it is required to detect, locate and then precisely spot-spray them with chemicals. In this paper, we present the application of YOLOv5m on radiometrically and gamma-corrected low resolution (1.2 Megapixel) multispectral imagery for detecting and locating VC plants growing in the middle of tasseling (VT) growth stage of cornfield. Our results show that VC plants can be detected with a mean average precision (mAP) of 79% and classification accuracy of 78% on images of size 1207 x 923 pixels at an average inference speed of nearly 47 frames per second (FPS) on NVIDIA Tesla P100 GPU-16GB and 0.4 FPS on NVIDIA Jetson TX2 GPU. We also demonstrate the application of a customized unmanned aircraft systems (UAS) for spot-spray applications based on the developed computer vision (CV) algorithm and how it can be used for near real-time detection and mitigation of VC plants growing in corn fields for efficient management of the boll weevil pests.

We took a look at the three types of region-based CNN (R-CNN) in the last article and we now know how they operate together with their downsides. With that in mind, let's explore two other object detection algorithms that are far more superior than region-based networks, so much so that they are widely used in real-time detection tasks. In case you forgot what the downside of Faster R-CNN is, it is a relatively slow detector which is unable to match the requirements of real-time detection. It does, however, have a small accuracy advantage if real-time detection is not required. Check out this article to compare the mAP and inference speed of all the detectors mentioned in this article.

Universal Adversarial Perturbations (UAPs) are a prominent class of adversarial examples that exploit the systemic vulnerabilities and enable physically realizable and robust attacks against Deep Neural Networks (DNNs). UAPs generalize across many different inputs; this leads to realistic and effective attacks that can be applied at scale. In this paper we propose HyperNeuron, an efficient and scalable algorithm that allows for the real-time detection of UAPs by identifying suspicious neuron hyper-activations. Our results show the effectiveness of HyperNeuron on multiple tasks (image classification, object detection), against a wide variety of universal attacks, and in realistic scenarios, like perceptual ad-blocking and adversarial patches. HyperNeuron is able to simultaneously detect both adversarial mask and patch UAPs with comparable or better performance than existing UAP defenses whilst introducing a significantly reduced latency of only 0.86 milliseconds per image. This suggests that many realistic and practical universal attacks can be reliably mitigated in real-time, which shows promise for the robust deployment of machine learning systems.