Image recoloring detection based on inter-channel correlation - Nuo CHEN; Shuren QI; Yushu ZHANG; Mingfu XUE; Zhongyun HUA (网络与信息安全学报)
Image recoloring is an emerging editing technique that can change the color style of an image by modifying pixel values.With the rapid proliferation of social networks and image editing techniques, recolored images have seriously hampered the authenticity of the communicated information.However, there are few works specifically designed for image recoloring.Existing recoloring detection methods still have much improvement space in conventional recoloring scenarios and are ineffective in dealing with hand-crafted recolored images.For this purpose, a recolored image detection method based on inter-channel correlation was proposed for conventional recoloring and hand-crafted recoloring scenarios.Based on the phenomenon that there were significant disparities between camera imaging and recolored image generation methods, the hypothesis that recoloring operations might destroy the inter-channel correlation of natural images was proposed.The numerical analysis demonstrated that the inter-channel correlation disparities can be used as an important discriminative metric to distinguish between recolored images and natural images.Based on such new prior knowledge, the proposed method obtained the inter-channel correlation feature set of the image.The feature set was extracted from the channel co-occurrence matrix of the first-order differential residuals of the differential image.In addition, three detection scenarios were assumed based on practical situations, including scenarios with matching and mismatching between training-testing data, and scenario with hand-crafted recoloring.Experimental results show that the proposed method can accurately identify recolored images and outperforms existing methods in all three hypothetical scenarios, achieving state-of-the-art detection accuracy.In addition, the proposed method is less dependent on the amount of training data and can achieve fairly accurate prediction results with limited training data.