Appeal No. 0750: Paul A. Grim v. Division of Mineral Resources Management

Chief\u27s Order 2005-2

The square root law of steganographic capacity.

There are a number of recent information theoretic results demonstrating (under certain conditions) a sublinear relationship between the number of cover objects and their total steganographic capacity. in this paper we explain how these results may be adapted to the steganographic capacity of a single cover object, which under the right conditions should be proportional to the square root of the cover size. Then we perform some experiments using three genuine steganography methods in digital images, covering both spatial and DOT domains. Measuring detectability under four different steganalysis methods, for a, variety of payload and cover sizes, we observe close accordance with a square root law. Copyright 2008 ACM

The square root law of steganographic capacity.

There are a number of recent information theoretic results demonstrating (under certain conditions) a sublinear relationship between the number of cover objects and their total steganographic capacity. in this paper we explain how these results may be adapted to the steganographic capacity of a single cover object, which under the right conditions should be proportional to the square root of the cover size. Then we perform some experiments using three genuine steganography methods in digital images, covering both spatial and DOT domains. Measuring detectability under four different steganalysis methods, for a, variety of payload and cover sizes, we observe close accordance with a square root law. Copyright 2008 ACM

JPEG-Compatibility Steganalysis Using Block-Histogram of Recompression Artifacts

Abstract. JPEG-compatibility steganalysis detects the presence of embedding changes using the fact that the stego image was previously JPEG compressed. Following the previous art, we work with the difference between the stego image and an estimate of the cover image obtained by recompression with a JPEG quantization table estimated from the stego image. To better distinguish recompression artifacts from embedding changes, the difference image is represented using a feature vector in the form of a histogram of the number of mismatched pixels in 8 × 8 blocks. Three types of classifiers are built to assess the detection accuracy and compare the performance to prior art: a clairvoyant detector trained for a fixed embedding change rate, a constant false-alarm rate detector for an unknown change rate, and a quantitative detector. The proposed approach offers significantly more accurate detection across a wide range of quality factors and embedding operations, especially for very small change rates. The technique requires an accurate estimate of the JPEG compression parameters.

Detection of classifier inconsistencies in image steganalysis

In this paper, a methodology to detect inconsistencies in classification-based image steganalysis is presented. The proposed approach uses two classifiers: the usual one, trained with a set formed by cover and stego images, and a second classifier trained with the set obtained after embedding additional random messages into the original training set. When the decisions of these two classifiers are not consistent, we know that the prediction is not reliable. The number of inconsistencies in the predictions of a testing set may indicate that the classifier is not performing correctly in the testing scenario. This occurs, for example, in case of cover source mismatch, or when we are trying to detect a steganographic method that the classifier is no capable of modelling accurately. We also show how the number of inconsistencies can be used to predict the reliability of the classifier (classification errors)