Oct 2026· International Journal of Research Publication and Reviews· Vol 7, pp. 5760-5765· 0 citations
TL;DR
Fakescope is discussed, an AI-driven system for deepfake detection and verification, addressing the challenges posed by realistic, manipulated images and videos, aiming to establish a preliminary screening tool for suspicious media.
Abstract
The paper discusses Fakescope, an AI-driven system for deepfake detection and verification, addressing the challenges posed by realistic, manipulated images and videos. While deepfakes have legitimate uses, their potential for misuse raises concerns about misinformation and digital impersonation. The system features a web interface built with HTML, CSS, and JavaScript, supported by a Python Flask backend and Xception-based convolutional neural network. For image classification, the model processes 224x224 pixel images and outputs a binary score. Video analysis is performed by extracting frames with OpenCV, analyzing them individually, and aggregating results for a collective prediction. The paper outlines the problem statement, objectives, related work, methodology, model architecture, implementation, and evaluation framework, aiming to establish a preliminary screening tool for suspicious media, with effectiveness to be validated through independent testing and classification metrics. Key terms include Deepfake Detection, Artificial Intelligence, and Convolutional Neural Networks.
The authors present a hybrid system of Convolutional Neural Networks - Long Short-Term Memory (CNN-LSTM) for an AI-based deepfake and digital authenticity verification system which is more robust and performs better in detection, improving on the traditional CNN-based approaches.
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