Abstract
The standard local binary pattern (LBP) operator shows its versatility in performing image classification-related tasks, including texture analysis, object recognition, and steganalysis. However, a conventional well-designed scheme utilizing LBP operator and histogram-based features does not have obvious advantage when compared with the well-known steganalytic scheme spatial rich model (SRM). In this paper, we propose an adapted LBP version, called threshold LBP (TLBP), to reveal the artifacts caused by data embedding. In the proposed steganalytic scheme, the TLBP operation is performed on residual images which are obtained by using a set of high-order derivative filters to capture intricate relationships among pixels. After performing TLBP operation, second order co-occurrence matrix features are formed and then processed with aggregation and non-linear mapping for boosting feature effectiveness. Experimental results show that the proposed TLBP features prevail over SRM features under various steganographic conditions.
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