Exploiting texture characteristics and spatial correlations for robustness metric of data hiding with noisy transmission

Abstract

Data hiding aims to embed a secret message into a digital object such as image by slightly modifying the object content without arousing noticeable artefacts. The resultant object containing hidden information will be sent to a desired receiver via some insecure channels, e.g. images transmitted through noisy channel, social networks are vulnerable to unknown pollution or compression by a third party, which may lead the transmitted objects to be attacked such that the reconstructed message has a significant error rate. It therefore requires us to use robust embedding strategies for data hiding to realise reliable message retrieval. To this end, in this paper, a metric model to estimate the robustness of data hiding for noisy transmission based on the statistical characteristics of cover and embedding operation is presented, the former is mainly reflected by spatial frequency and texture feature, and the latter embedding operation is mainly reflected by embedding modification. The goal is to ensure that both statistical characteristics and embedding operation can be used to maximise the embedding robustness. To the best knowledge, it is the first time to estimate robustness before data hiding by a special metric model. Experimental results show that, by combining the proposed metric model in three classical data hiding methods, i.e. BPS, DE and QIM, the robustness can be significantly improved, which demonstrates its superiority and applicability.

SNENet: An adaptive stego noise extraction network using parallel dilated convolution for JPEG image steganalysis

“…The representative hand-crafted feature-based methods include SRM [5], DCTR [6], PHARM [7], GFR [8], and so on. However, with the development of adaptive steganography algorithms [9,10], effective steganalysis results are increasingly difficult to be achieved by these steganalytic methods.…”