  Nowadays, people are sharing their pictures on online social networks (OSNs),
so OSN is a good platform for Steganography. But OSNs usually perform JPEG
compression on the uploaded image, which will invalidate most of the existing
steganography algorithms. Recently, some works try to design robust
steganography which can resist JPEG compression, such as Dither
Modulation-based robust Adaptive Steganography (DMAS) and Generalized dither
Modulation-based robust Adaptive Steganography (GMAS). They relieve the problem
that the receivers cannot extract the message correctly when the quality factor
of channel JPEG compression is larger than that of cover images. However, they
only can realize limited resistance to detection and compression due to robust
domain selection. To overcome this problem, we meticulously explore three lossy
operations in the JPEG recompression and discover that the key problem is
spatial overflow. Then two preprocessing methods Overall Scaling (OS) and
Specific Truncation (ST) are presented to remove overflow before message
embedding as well as generate a reference image. The reference image is
employed as the guidance to build asymmetric distortion for removing overflow
during embedding. Experimental results show that the proposed methods
significantly surpass GMAS in terms of security and achieve comparable
robustness.
