Abstract:

Image forgery detection has been a critical area of research in recent years, as digital images can be easily manipulated using various tools and techniques. This paper proposes an approach to detect image forgery using Efficient LBP and CNN. Efficient LBP is a texture descriptor that extracts local features from images, while CNN is a deep learning algorithm that can learn hierarchical features. The combination of these two techniques can effectively detect various types of image forgery. In this paper, we provide an overview of Image Forgery Detection using Efficient LBP and CNN, including its advantages, limitations, and future directions. We also review some recent studies that have used this approach and discuss their results. The proposed approach shows promising results in detecting image forgery, and it can be used to ensure the authenticity of images in various applications

Description:

Convolutional Neural Network, Local Binary Pattern, LBPNet, Deep Learning