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This paper deals with the problem of batch steganography and pooled steganalysis when the sender uses a steganography detector to spread chunks of the payload across a bag of cover images while the Warden uses a possibly different detector for her pooled steganalysis. We investigate how much information can be communicated with increasing bag size at a fixed statistical detectability of Warden’s detector. Specifically, we are interested in the scaling exponent of the secure payload. We approach this problem both theoretically from a statistical model of the soft output of a detector and practically using experiments on real datasets when giving both actors different detectors implemented as convolutional neural networks and a classifier with a rich model. While the effect of the detector mismatch depends on the payload allocation algorithm and the type of mismatch, in general the mismatch decreases the constant of proportionality as well as the exponent. This stays true independently of who has the superior detector. Many trends observed in experiments qualitatively match the theoretical predictions derived within our model. Finally, we summarize our most important findings as lessons for the sender and for the Warden. 

In batch steganography, the sender spreads the secret payload among multiple cover images forming a bag. The question investigated in this paper is how many and what kind of images the sender should select for her bag. We show that by forming bags with a bias towards selecting images that are more difficult to steganalyze, the sender can either lower the probability of being detected or save on bandwidth by sending a smaller bag. These improvements can be quite substantial. Our study begins with theoretical reasoning within a suitably simplified model. The findings are confirmed on experiments with real images and modern steganographic and steganalysis techniques. 

Abstract--- The JPEG compatibility attack is a steganalysis method for detecting messages embedded in the spatial representation of images under the assumption that the cover is a decompressed JPEG. This paper focuses on improving the detection accuracy for the difficult case of high JPEG qualities and content-adaptive stego algorithms. Close attention is paid to the robustness of the detection with respect to the JPEG compressor and DCT coefficient quantizer. A likelihood ratio detector derived from a model of quantization errors of DCT coefficients in the recompressed image is used to explain the main mechanism responsible for detection and to understand the results of experiments. The most accurate detector is an SRNet trained on a two-channel input consisting of the image and its SQ error. The detection performance is contrasted with state of the art on four content-adaptive stego methods, wide range of payloads and quality factors.