Weighted stego-image steganalysis for naive content-adaptive embedding

Abstract

Weighted stego-image (WS) steganalysis is the state of the art for estimating LSB replacement steganography in spatial domain images. However, the most powerful WS variants designed against random uniform embedding perform poorly against content-adaptive steganography. As a remedy, we propose a novel variant of WS which is specialized in detecting small payloads hidden exclusively in the least detectable spots of a cover, benchmark its performance against known methods, and experimentally investigate the influence of the choice of the adaptivity criterion, i. e., the function that identifies supposedly secure spots in a heterogeneous cover. We find that adaptivity criteria which are hard to recover from the stego image alone provide stronger security against our specialized WS method.