Generative AI and synthetic media generation tools have enabled widespread media manipulation tools and raised important privacy concerns with misinformation, identity fraud and the verification of authenticity of media. Most of the current convolution-based deepfake detection methods are hard to be deployed in real scenarios and hard to be interpretable, especially because they have limited ability to capture long-range spatial dependency. It introduces an explainable deepfake face image detection framework based on a vision transformer network for performing powerful binary classification of manipulated and real facial images and an explainable face image localization framework for localizing deepfake image faces. The proposed system involves a transformer-encoder backbone for extracting features through a patches-wise process, which proves suitable for modeling the subtle changes of features when the processes of manipulating the image are designed. To make the network more interpretable, and aid the understanding of the transformer attention distribution as well as localization of manipulated facial regions, a dedicated attention rollout mechanism is embedded. A dedicated rollout mechanism for attention distribution of the transformer and heatmap generating and attention spatial localization are incorporated to improve the interpretability of the network. The framework comprises an end-to-end inference pipeline, such as image preprocessing, estimation of confidence scores, fake-real classification, generation of explainable visualization and storage of prediction history using an integrated database system. An experimental evaluation shows the system can effectively detect deepfakes while also providing accurate local information as justification for classification decisions, contributing to transparency, reliability and trust towards automated synthetic media detection systems.
K. Phani, Shaik Mahaboob, Jailan PG Student et al.· 2026 International Conferenc...· 0 citations
Large-scale DDoS attacks remain a serious threat to today's networked systems, which aim to make services unavailable by sending a massive amount of traffic. The traditional detection methods are mostly about attack categorization and are not that context-aware or actionable in providing support to security analysts. We propose SentinelsGuard AI, a self-learning DDoS classification and mitigation system that is built from a combination of machine learning, retrieval-augmented knowledge, and LLM-based reasoning in one platform. The proposed system is realized as a Flask-based web application, which processes the network flow statistics data, extracted from incoming traffic, and transforms the data to generate representative features of the network flows, before detecting the attacks using the XGBoost classifier. After classification, the framework accesses domain-specific attack intelligence from a structured Retrieval-Augmented Generation (RAG) knowledge base with profiles of multiple categories of DDoS attacks. This contextual information is used with LLM-enabled reasoning to derive comprehensible security explanations, determine the severity of the attack, and suggest the firewall mitigation rules that can be applied. The framework stores analysis data in a SQLite database for operational monitoring, and displays security events in an interactive dashboard that includes traffic visualization, attack logs, and threat summaries. The architecture proposed brings statistical learning together with contextual knowledge of cybersecurity and attempts to bridge the gap between automatic attack detection and explainable incident response. It integrates all the key features of a classification-based network security decision support platform into a single stream, enabling a classification-based approach to network security to become more interpretable and usable in reality: persistent logging, visualization, knowledge retrieval, reasoning, and system recommendation of mitigations.
K. Phani, P. Karthik, PG Student et al.· 2026 International Conferenc...· 0 citations