Dual-Branch Convolutional Framework for Spatial and Frequency-Based Image Forgery Detection

Authors: Naman Tyagi

Published: 2025-09-05 17:41:57+00:00

AI Summary

This paper proposes a dual-branch convolutional neural network for image forgery detection, combining spatial and frequency-based features extracted from the input image. The features are fused and compared within a Siamese network to generate 64-dimensional embeddings for classification, achieving 77.9% accuracy on the CASIA 2.0 dataset.

Abstract

With a very rapid increase in deepfakes and digital image forgeries, ensuring the authenticity of images is becoming increasingly challenging. This report introduces a forgery detection framework that combines spatial and frequency-based features for detecting forgeries. We propose a dual branch convolution neural network that operates on features extracted from spatial and frequency domains. Features from both branches are fused and compared within a Siamese network, yielding 64 dimensional embeddings for classification. When benchmarked on CASIA 2.0 dataset, our method achieves an accuracy of 77.9%, outperforming traditional statistical methods. Despite its relatively weaker performance compared to larger, more complex forgery detection pipelines, our approach balances computational complexity and detection reliability, making it ready for practical deployment. It provides a strong methodology for forensic scrutiny of digital images. In a broader sense, it advances the state of the art in visual forensics, addressing an urgent requirement in media verification, law enforcement and digital content reliability.