Robust Image-Adaptive Data Hiding Using Erasure and Error Correction

Abstract

Information-theoretic analyses for data hiding prescribe embedding the hidden data in the choice of quantizer for the host data. In this paper, we propose practical realizations of this prescription for data hiding in images, with a view to hiding large volumes of data with low perceptual degradation. The hidden data can be recovered reliably under attacks, such as compression and limited amounts of image tampering and image resizing. The three main findings are as follows. 1) In order to limit perceivable distortion while hiding large amounts of data, hiding schemes must use image-adaptive criteria in addition to statistical criteria based on information theory. 2) The use of local criteria to choose where to hide data can potentially cause desynchronization of the encoder and decoder. This synchronization problem is solved by the use of powerful, but simple-to-implement, erasures and errors correcting codes, which also provide robustness against a variety of attacks. 3) For simplicity, scalar quantization-based hiding is employed, even though information-theoretic guidelines prescribe vector quantization-based methods. However, an information-theoretic analysis for an idealized model is provided to show that scalar quantization-based hiding incurs approximately only a 2-dB penalty in terms of resilience to attack.

YASS: Yet Another Steganographic Scheme That Resists Blind Steganalysis

“…One issue with hiding data in random locations is the possibility of encountering errors in the hidden bits due to the fact that the stego image must be shipped or advertised in a standard format such as JPEG. This is dealt with by the use of erasures and error correction coding framework previously employed in [18]. In the next section, we describe yet another steganographic scheme (YASS), a JPEG stegosystem based on the aforementioned framework.…”

“…Data is hidden in a predetermined band of low frequency AC coefficients using quantization index modulation. For maintaining perceptual transparency, we do not hide in coefficients that quantize to zero by the JPEG quantizer (following the selective embedding in coefficients scheme proposed in [18]). …”

“…In order to deal with the errors caused in the image due to JPEG compression, we use a coding framework using repeat-accumulate (RA) codes [25], similar to that proposed in [18]. This framework also allows us to hide in an adaptive fashion, avoiding coefficients that quantize to zero so as to control the perceptual distortion to the image.…”

“…Although data is hidden in randomly chosen blocks in the image, the image must be advertised in JPEG format. This leads to errors in the recovered data bits, which are dealt with by using erasure and error correcting codes in a manner similar to [18].…”

Forensic data hiding optimized for JPEG 2000

“…The use of perceptual shaping in data hiding improves performance in terms of lower perceptual distortion for fixed payload and robustness [2], [3]. This performance is due to the incorporation of how the human visual system processes visual stimuli by exploiting the image statistics.…”