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.

翻译：除了传统的基于统计的方法外,最近还利用了深层次的学习来设计一个表现良好的估测器,培训了革命神经网络(CNN)模型,以解决作为标准回归问题的估算问题。在本文件中,我们提议使用Smillic化的CNN结构来解决估算问题,利用量化系数的整数性质,并使用适当的损失函数进行培训,同时考虑到估算的准确性和中位平方误差。这种方法在一般操作条件下工作的能力,即第二压缩格与第一次压缩格以及前二次压缩格的JPEG质量相结合的能力,在实际应用中非常相关,因为这些信息事先不为人所知。结果证实了与基于统计分析和深层学习回归的先进方法相比,拟议技术的有效性。