Copy-move Forgery Detection based on Convolutional Kernel Network

TL;DR

This paper introduces a novel copy-move forgery detection method utilizing a Convolutional Kernel Network, leveraging deep learning for high accuracy and efficiency, especially with GPU acceleration, outperforming existing techniques.

Contribution

The paper presents a data-driven local descriptor based on Convolutional Kernel Network, reformulated for parallel GPU processing, with a segmentation-based keypoints strategy for improved forgery detection.

Findings

Achieves state-of-the-art detection performance.

Demonstrates robustness across various conditions.

Offers high efficiency through GPU implementation.

Abstract

In this paper, a copy-move forgery detection method based on Convolutional Kernel Network is proposed. Different from methods based on conventional hand-crafted features, Convolutional Kernel Network is a kind of data-driven local descriptor with the deep convolutional structure. Thanks to the development of deep learning theories and widely available datasets, the data-driven methods can achieve competitive performance on different conditions for its excellent discriminative capability. Besides, our Convolutional Kernel Network is reformulated as a series of matrix computations and convolutional operations which are easy to parallelize and accelerate by GPU, leading to high efficiency. Then, appropriate preprocessing and postprocessing for Convolutional Kernel Network are adopted to achieve copy-move forgery detection. Particularly, a segmentation-based keypoints distribution strategy is proposed and a GPU-based adaptive oversegmentation method is adopted. Numerous experiments are conducted to demonstrate the effectiveness and robustness of the GPU version of Convolutional Kernel Network, and the state-of-the-art performance of the proposed copy-move forgery detection method based on Convolutional Kernel Network.