ELA-Conv: Forgery Detection in Digital Images Based on ELA and CNN

ELA-Conv: Forgery Detection in Digital Images Based on ELA and CNN

연구 분야: Strategies

학회: International Conference on Recent Trends in Image Processing and Pattern Recognition

Due to the rapid growth and evolution of smart devices like smartphones and cameras, a huge amount of digital data is generated in the form of digital images. Digital images are foundational pillars of data because they are a reliable source of information because of their visual appeal and information. Modern software and technologies have opened the doors for new and creative ways to forge or tamper images. Digital image forgery means manipulating the digital image to suppress some meaningful and factual information inside the image or misguide any concerned organization. The detection of forged images is inspired by the requirement for authenticity and integrity maintenance. Researchers have used Deep Learning (DL) techniques for the detection of image tampering and forgeries. This paper proposes an image forgery detection method based on Error Level Analysis (ELA) and Convolutional Neural Network (CNN). ELA is an image processing technique used for detecting inconsistencies and potential manipulation using compression artefacts of images with lossy compression. CNN are a class of neural networks specialized for their superior performance with images. The proposed method uses ELA, which is pipelined to a CNN model. The analysis is performed on the standard CASIAv2 dataset which consists of 7491 authentic images and 5123 forged images. The proposed method attains a superior accuracy of 94% and an F-score of 94%. The result reveals that the proposed model outperforms other pre-trained baseline models like VGG16 and VGG19.