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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.
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This content will become publicly available on February 2, 2026
Transformers for Pooled Steganalysis
In batch steganography, the sender communicates a secret message by hiding it in a bag of cover objects. The adversary performs the so-called pooled steganalysis in that she inspects the entire bag to detect the presence of secrets. This is typically realized by using a detector trained to de- tect secrets within a single object, applying it to all objects in the bag, and feeding the detector outputs to a pooling function to obtain the final detection statistic. This paper deals with the problem of building the pooler while keep- ing in mind that the Warden will need to be able to de- tect steganography in variable size bags carrying variable payload. We propose a flexible machine learning solution to this challenge in the form of a Transformer Encoder Pooler, which is easily trained to be agnostic to the bag size and payload and offers a better detection accuracy than pre- viously proposed poolers.
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- Award ID(s):
- 2324991
- PAR ID:
- 10621662
- Publisher / Repository:
- IS&T
- Date Published:
- Journal Name:
- Electronic Imaging
- Volume:
- 37
- Issue:
- 4
- ISSN:
- 2470-1173
- Page Range / eLocation ID:
- 299-1 to 299-10
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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