Boosting CNN-based primary quantization matrix estimation of double JPEG images via a classification-like architecture

Benedetta Tondi, Andrea Costranzo, Dequ Huang, Bin Li

The problem of estimation of the primary quantization matrix in double JPEG images is of relevant importance in several applications, and in particular, for splicing localization. In addition to traditional statistical-based approaches, recently, deep learning has been exploited to design a well performing estimator, by training a Convolutional Neural Network (CNN) model to solve the estimation as a standard regression problem. In this paper, we propose the use of a simil-classification CNN architecture to solve the estimation, by exploiting the integer nature of the quantization coefficients, and using a proper loss function for training, that takes into account both the accuracy and the Mean Square Error of the estimation. The capability of the method to work under general operative conditions, regarding the alignment of the second compression grid with the one of first compression and the combinations of the JPEG qualities of former and second compression, is very relevant in practical applications, where these information are unknown a priori. Results confirm the effectiveness of the proposed technique, compared to the state-of-the art methods based on statistical analysis and deep learning regression.

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