arXiv subjects

Lata B T

Publications and source records attributed to Lata B T.

6 recordsLinked to original sources

arXiv2026

ForensicNet: Lightweight Attention-Enhanced MobileNetV2 for Automated Face Identification

In forensic environments, automated identification of perpetrators is difficult due to pose changes, changes in light, occlusion, and lack of labeled data. This paper presents ForensicNet, a lightweight deep learning framework for forensic face recognition that enhances attention. The suggested model combines the MobileNetV2 backbone with Convolutional Block Attention Modules (CBAM) to improve the learning of discriminative features while maintaining computational speed. A two-phase transfer learning strategy with adaptive layer unfreezing is used to improve domain adaptation and reduce overfitting. This study used publicly available datasets such as LFW and SCFace, with 15,000 facial images spanning 68 identity classes. The proposed model outperforms baseline architectures such as AlexNet, ResNet-50, and MobileNetV2, with an accuracy of 92.4%, a precision of 90.8%, and a recall of 89.5%. Additionally, the framework requires only 2.1 GFLOPs per inference, and hence can be used in real-time forensic surveillance applications.

cs.CV↗

arXiv2026

Attention-Guided Efficientnet Architecture For Precise Criminal Identification in Surveillance Images

Criminal identification from surveillance imagery has become a critical research area in intelligent forensic surveillance systems due to the increasing deployment of CCTV cameras in public and private environments. However, surveillance-based face recognition remains highly challenging because of low image resolution, illumination variation, motion blur, pose changes, facial occlusion, and background clutter. To address these limitations, this paper proposes an Attention-Guided EfficientNet (AG-EfficientNet) framework for precise criminal identification in surveillance images. The proposed framework integrates EfficientNet-B0 with Convolutional Block Attention Modules (CBAM) to enhance discriminative facial feature learning under degraded surveillance conditions. In addition, a multi-scale surveillance feature fusion strategy is introduced to preserve both local texture information and high-level semantic identity representations. A hybrid Softmax-Triplet optimization mechanism is further employed to improve inter-class separability and intra-class compactness for robust criminal identity discrimination. The proposed framework was experimentally evaluated using the Labeled Faces in the Wild (LFW) and SCFace datasets. Experimental results demonstrate that the proposed AG-EfficientNet framework achieved superior surveillance recognition performance with an identification accuracy of 98.2%, Precision of 97.9%, Recall of 97.6%, F1-Score of 97.7%, and ROC-AUC of 0.99, outperforming conventional deep learning architectures including AlexNet, VGG16, ResNet50, MobileNetV2, and standard EfficientNet-B0. Furthermore, Grad-CAM visualization and ablation analysis confirm the effectiveness of the proposed attention-guided feature learning strategy.

cs.CV↗

arXiv2026

DCBA: Detection of Collaborative Black-Hole Attacks in Connected Dominated Set using Baiting Process

Mobile Ad-hoc Network (MANET) is temporary and dynamic network topology, wherein nodes are mobile in nature and distributed randomly in a network area. In MANET, nodes cooperate with each other to operate and forward data through multihop communication between source and destination. MANET is exposed to different types of attacks due to absence of central administration. However, some nodes decline to cooperate, misbehaves and appears to be malicious affecting network functionality and connectivity. Providing security and identifying malicious node has become one of the challenging research topics in MANET. Black-hole attack is considered to be most popular attack that degrades the overall network performance. Black-hole node falsely advertises the shortest path to destination intentionally to disrupt the network communication resulting in packet drop. In collaborative black-hole attacks, multiple black-hole nodes cooperate and launch attacks in order to degrade network reliability. In this article we propose a Lightweight technique to detect and isolate Collaborative Black-Hole attacks (LW-CBH) by enhancing existing AODV routing protocol. In this scheme a timer based baiting process and reverse tracing setup is used to detect malicious node through control status message in MAC layer which are Reply Sequence (R-SEQ) and Code Sequence (C-SEQ) message of connected dominated set of nodes. However existing AODV routing protocol fails to detect malicious node during dynamic topology changing in MANET. Simulation of proposed technique is performed using discrete event simulator tool NS-2.35. The simulation results are evaluated for throughput, packet delivery ratio, average end-to-end delay and normalized routing overhead.

cs.CR↗

arXiv2026

Identifying Culprits Through Deep Deterministic Policy Gradient Deep Learning Investigation

In the world of AI and advanced technologies investigation aspects identification of a crime or criminal plays a major problem. In this research we focus on a Conventional ways of implicating criminal investigations usually rely on limited data analysis. Finding an optimal and efficient method that will effectively identify criminals from complex datasets and minimise false positives and false negatives is the considered as a challenge. The main novelty approach of this work is based on the deep learning algorithm Deep Deterministic Policy Gradient (DDPG) is presented in this paper. We train the DDPG model with a dataset of crime scene material, witness statements and suspect profiles. The algorithm uses features to maximise the likelihood of identifying the offender while minimising the noise impact and irrelevant data. We show the efficacy of the proposed method, where DDPG identified criminals with an amazing accuracy of 95% than other several existing methods.

cs.AI↗

arXiv2026

Optimized Culprit Identification Using Mobilenet and Attention Mechanisms

Automated culprit identification in surveillance systems is a critical task that requires high accuracy along with computational efficiency for real-time deployment. In this paper, an optimized deep learning framework is proposed using a lightweight MobileNet architecture integrated with channel and spatial attention mechanisms. The proposed model enhances feature representation by selectively focusing on the most discriminative regions while suppressing irrelevant background information, thereby improving identification performance. The framework incorporates efficient preprocessing, attention based feature refinement, and a robust classification strategy optimized using the Adam Optimizer. Experiments were conducted on benchmark face recognition datasets, including Labelled Faces in the Wild (LFW), CASIA-WebFace, and a subset of VGGFace2, under realistic conditions with variations in illumination, pose, and occlusion. The results demonstrate that the proposed model achieves a high classification accuracy of 97.8%, outperforming conventional models such as baseline CNN, ResNet, and standard MobileNet. The confusion matrix analysis indicates strong class-wise discrimination with minimal misclassification, while ROC-AUC evaluation confirms robust performance across all classes. Additionally, the proposed approach maintains low computational complexity and reduced inference time, making it suitable for real-time surveillance and edge-based applications.

cs.CV↗

arXiv2013

Multiple Domain Secure Routing for Wireless Sensor Networks

Secure Transmission of data packets in Wireless Sensor Networks is an important area of Research. There is a possibility of an attacker creating security holes in the network. Hence, network security and reliability can be achieved by discovering random multiple paths using multiple domains, and forwarding data packets from the source node to the destination node. We have designed, Multiple Domain Routing with Overlap of Nodes (MDRON) and Multiple Domain Routing Without Overlap of Nodes (MDRWON) algorithms, in which packets follow multiple optimized paths simultaneously. The Special node algorithm searches the node which has maximum power and these nodes are used for transferring the packet from one domain to another domain. Simulation results using MATLAB shows that performance is better than Purely Random Propagation (PRP) and Non Repetitive Random Propagation(NRRP) Algorithms.

cs.CR↗