Recently,steganalytic methods based on deep learning have achieved much better performance than traditional methods based on handcrafted features.However,most existing methods based on deep learning are specially desi...Recently,steganalytic methods based on deep learning have achieved much better performance than traditional methods based on handcrafted features.However,most existing methods based on deep learning are specially designed for one image domain(i.e.,spatial or JPEG),and they often take long time to train.To make a balance between the detection performance and the training time,in this paper,we propose an effective and relatively fast steganalytic network called US-CovNet(Universal Steganalytic Covariance Network)for both{the}spatial and JPEG domains.To this end,we carefully design several important components of{US-CovNet}that will significantly affect the detection performance,including the high-pass filter set,the shortcut connection and the pooling{layer}.Extensive experimental results show that compared with the current best steganalytic networks(i.e.,SRNet and J-YeNet),{US-CovNet}can achieve the state-of-the-art results for detecting spatial steganography and have competitive performance for detecting JPEG steganography.For example,the detection accuracy of US-CovNet is at least 0.56%higher than that of SRNet in the spatial domain.In the JPEG domain,US-CovNet performs slightly worse than J-YeNet in some cases with the degradation of less than 0.78%.However,the training time of US-CovNet is significantly reduced,which is less than 1/4 and 1/2 of SRNet and J-YeNet respectively.展开更多
基金The work was supported in part by the National Natural Science Foundation of China under Grant No.61972430the Natural Science Foundation of Guangdong Province of China under Grant No.2019A1515011549the Guangdong Natural Science Key Field Project under Grant No.2019KZDZX1008.
文摘Recently,steganalytic methods based on deep learning have achieved much better performance than traditional methods based on handcrafted features.However,most existing methods based on deep learning are specially designed for one image domain(i.e.,spatial or JPEG),and they often take long time to train.To make a balance between the detection performance and the training time,in this paper,we propose an effective and relatively fast steganalytic network called US-CovNet(Universal Steganalytic Covariance Network)for both{the}spatial and JPEG domains.To this end,we carefully design several important components of{US-CovNet}that will significantly affect the detection performance,including the high-pass filter set,the shortcut connection and the pooling{layer}.Extensive experimental results show that compared with the current best steganalytic networks(i.e.,SRNet and J-YeNet),{US-CovNet}can achieve the state-of-the-art results for detecting spatial steganography and have competitive performance for detecting JPEG steganography.For example,the detection accuracy of US-CovNet is at least 0.56%higher than that of SRNet in the spatial domain.In the JPEG domain,US-CovNet performs slightly worse than J-YeNet in some cases with the degradation of less than 0.78%.However,the training time of US-CovNet is significantly reduced,which is less than 1/4 and 1/2 of SRNet and J-YeNet respectively.
