DeepSentry Platform: An Asymmetric AI System for Misinformation Detection - Trung, Pham Duy; Minh, Pham Ngoc; Lam, Bui Thu (VNU Journal of Science: Computer Science and Communication Engineering; Article in PressDO - 10.25073/2588-1086/vnucsce.7028, 2026)
Abstract: Deployment of deep learning–based misinformation detection systems frequently su ff ers from severe resource contention when heterogeneous computational workloads share the same hardware. CPU-intensive preprocessing operations, such as video decoding and face extraction, interfere with GPU-intensive neural network inference, leading to GPU underutilization and Head-of-Line blocking in monolithic architectures. This paper introduces DeepSentry, a microservice-based detection platform designed to mitigate this bottleneck through asymmetric architectural decoupling. In this context, asymmetry is defined as a non-uniform architectural decomposition in which CPUbound preprocessing services and GPU-bound inference services are intentionally isolated and scaled independently, rather than replicated symmetrically as identical service instances. By separating CPU-bound ingestion services from GPU-bound inference engines via message queuing and an inference server, the proposed platform enables independent scaling and asynchronous execution of heterogeneous resources. A proof-of-concept implementation integrating ResNet34 for image deepfake detection with heatmap generation and BiLSTM for fake news classification demonstrates that asymmetric microservice architectures significantly improve hardware utilization through dynamic batching and asynchronous orchestration, e ff ectively resolving the resource contention inherent in monolithic deployments. Keywords: Resource contention, GPU starvation, Microservices, NVIDIA Triton, Deepfake detection, Fake news detection.