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Steganalysis by Subtractive Pixel Adjacency Matrix
"... This paper presents a novel method for detection of steganographic methods that embed in the spatial domain by adding a low-amplitude independent stego signal, an example of which is LSB matching. First, arguments are provided for modeling differences between adjacent pixels using first-order and se ..."
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Cited by 39 (21 self)
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This paper presents a novel method for detection of steganographic methods that embed in the spatial domain by adding a low-amplitude independent stego signal, an example of which is LSB matching. First, arguments are provided for modeling differences between adjacent pixels using first-order and second-order Markov chains. Subsets of sample transition probability matrices are then used as features for a steganalyzer implemented by support vector machines. The accuracy of the presented steganalyzer is evaluated on LSB matching and four different databases. The steganalyzer achieves superior accuracy with respect to prior art and provides stable results across various cover sources. Since the feature set based on second-order Markov chain is highdimensional, we address the issue of curse of dimensionality using a feature selection algorithm and show that the curse did not occur in our experiments.
R.: Synthesis of color filter array pattern in digital images
- Proceedings of SPIEIS&T Electronic Imaging: Media Forensics and Security XI. Volume 7254. (2009
"... We propose a method to synthetically create or restore typical color filter array (CFA) pattern in digital images. This can be useful, inter alia, to conceal traces of manipulation from forensic techniques that analyze the CFA structure of images. For continuous signals, our solution maintains optim ..."
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Cited by 16 (4 self)
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We propose a method to synthetically create or restore typical color filter array (CFA) pattern in digital images. This can be useful, inter alia, to conceal traces of manipulation from forensic techniques that analyze the CFA structure of images. For continuous signals, our solution maintains optimal image quality, using a quadratic cost function; and it can be computed efficiently. Our general approach allows to derive even more efficient approximate solutions that achieve linear complexity in the number of pixels. The effectiveness of the CFA synthesis as tamper-hiding technique and its superior image quality is backed with experimental evidence on large image sets and against state-of-the-art forensic techniques. This exposition is confined to the most relevant ‘Bayer’-grid, but the method can be generalized to other layouts as well. 1.
Weighted stego-image steganalysis for JPEG covers
- INFORMATION HIDING. LNCS 5284
, 2008
"... This paper contains two new results for the quantitative detector of LSB replacement steganography based on a weighted stegoimage (WS). First, for spatial domain steganalysis, a variant of the WS method is known to be highly accurate only when cover images have never been subject to lossy compress ..."
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Cited by 15 (2 self)
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This paper contains two new results for the quantitative detector of LSB replacement steganography based on a weighted stegoimage (WS). First, for spatial domain steganalysis, a variant of the WS method is known to be highly accurate only when cover images have never been subject to lossy compression. We propose a new variant of WS which increases the accuracy for JPEG pre-processed covers by one order of magnitude, thus leaving behind the best structural detectors which were known to be more robust on JPEG pre-compressed covers than WS. Second, we explain why WS-like estimators can also detect LSB replacement steganography in the transformed domain, and derive a reduced-form estimator for JSteg steganography which has equal or slightly better performance than the currently best JSteg detectors.
Fast and Reliable Resampling Detection by Spectral Analysis of Fixed Linear Predictor Residue
"... This paper revisits the state-of-the-art resampling detector, which is based on periodic artifacts in the residue of a local linear predictor. Inspired by recent findings from the literature, we take a closer look at the complex detection procedure and model the detected artifacts in the spatial and ..."
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Cited by 14 (2 self)
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This paper revisits the state-of-the-art resampling detector, which is based on periodic artifacts in the residue of a local linear predictor. Inspired by recent findings from the literature, we take a closer look at the complex detection procedure and model the detected artifacts in the spatial and frequency domain by means of the variance of the prediction residue. We give an exact formulation on how transformation parameters influence the appearance of periodic artifacts and analytically derive the expected position of characteristic resampling peaks. We present an equivalent accelerated and simplified detector, which is orders of magnitudes faster than the conventional scheme and experimentally shown to be comparably reliable.
Steganalysis of content-adaptive steganography in spatial domain
- Information Hiding, 13th International Workshop, Lecture Notes in Computer Science
, 2011
"... Abstract. Content-adaptive steganography constrains its embedding changes to those parts of covers that are difficult to model, such as textured or noisy regions. When combined with advanced coding techniques, adaptive steganographic methods can embed rather large payloads with low statistical detec ..."
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Cited by 13 (8 self)
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Abstract. Content-adaptive steganography constrains its embedding changes to those parts of covers that are difficult to model, such as textured or noisy regions. When combined with advanced coding techniques, adaptive steganographic methods can embed rather large payloads with low statistical detectability at least when measured using feature-based steganalyzers trained on a given cover source. The recently proposed steganographic algorithm HUGO is an example of this approach. The goal of this paper is to subject this newly proposed algorithm to analysis, identify features capable of detecting payload embedded using such schemes and obtain a better picture regarding the benefit of adaptive steganography with public selection channels. This work describes the technical details of our attack on HUGO as part of the BOSS challenge. 1
Multi-Class Detector of Current Steganographic Methods for JPEG Format
"... The aim of this paper is to construct a practical forensic steganalysis tool for JPEG images that can properly analyze both single- and double-compressed stego images and classify them to selected current steganographic methods. Although some of the individual modules of the steganalyzer were previ ..."
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Cited by 10 (3 self)
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The aim of this paper is to construct a practical forensic steganalysis tool for JPEG images that can properly analyze both single- and double-compressed stego images and classify them to selected current steganographic methods. Although some of the individual modules of the steganalyzer were previously published by the authors, they were never tested as a complete system. The fusion of the modules brings its own challenges and problems whose analysis and solution is one of the goals of this paper. By determining the stego algorithm, this tool provides the first step needed for extracting the secret message. Given a JPEG image, the detector assigns it to 6 popular steganographic algorithms. The detection is based on feature extraction and supervised training of two banks of multi-classifiers realized using support vector machines. For accurate classification of single-compressed images, a separate multi-classifier is trained for each JPEG quality factor from a certain range. Another bank of multiclassifiers is trained for double-compressed images for the same range of primary quality factors. The image under investigation is first analyzed using a pre-classifier that detects selected cases of double-compression and estimates the primary quantization table. It then sends the image to the appropriate single- or double-compression multiclassifier. The error is estimated from more than 2.6 million images. The steganalyzer is also tested on two previously unseen methods to examine its ability to generalize.
Moving Steganography and Steganalysis from the Laboratory into the Real World
"... There has been an explosion of academic literature on steganography and steganalysis in the past two decades. With a few exceptions, such papers address abstractions of the hiding and detection problems, which arguably have become disconnected from the real world. Most published results, including b ..."
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Cited by 9 (7 self)
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There has been an explosion of academic literature on steganography and steganalysis in the past two decades. With a few exceptions, such papers address abstractions of the hiding and detection problems, which arguably have become disconnected from the real world. Most published results, including by the authors of this paper, apply “in laboratory conditions ” and some are heavily hedged by assumptions and caveats; significant challenges remain unsolved in order to implement good steganography and steganalysis in practice. This position paper sets out some of the important questions which have been left unanswered, as well as highlighting some that have already been addressed successfully, for steganography and steganalysis to be used in the real world.
Breaking HUGO – the process discovery
- Information Hiding, 13th International Workshop, Lecture Notes in Computer Science
, 2011
"... Abstract. This paper describes our experience with the BOSS competition in chronological order. The intention is to reveal all details of our effort focused on breaking HUGO – one of the most advanced steganographic systems ever published. We believe that researchers working in steganalysis of digit ..."
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Cited by 9 (6 self)
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Abstract. This paper describes our experience with the BOSS competition in chronological order. The intention is to reveal all details of our effort focused on breaking HUGO – one of the most advanced steganographic systems ever published. We believe that researchers working in steganalysis of digital media and related fields will find it interesting, inspiring, and perhaps even entertaining to read about the details of our journey, including the dead ends, false hopes, surprises, obstacles, and lessons learned. This information is usually not found in technical papers that only show the final polished approach. This work accompanies our other paper in this volume [9]. 1
Further Study on the Security of S-UNIWARD
"... Recently, a new steganographic method was introduced that utilizes a universal distortion function called UNI-WARD. The distortion between the cover and stego image is computed as a sum of relative changes of wavelet coefficients representing both images. As already pointed out in the original publi ..."
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Cited by 3 (2 self)
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Recently, a new steganographic method was introduced that utilizes a universal distortion function called UNI-WARD. The distortion between the cover and stego image is computed as a sum of relative changes of wavelet coefficients representing both images. As already pointed out in the original publication, the selection channel of the spatial version of UNIWARD (the version that hides messages in pixel values called S-UNIWARD) exhibits unusual properties – in highly textured and noisy regions the embedding probabilities form interleaved streaks of low and high embedding probability. While the authors of UNIWARD themselves hypothesized that such an artifact in the embedding probabilities may jeopardize its security, experiments with state-of-the-art rich models did not reveal any weaknesses. Using the fact that the cover embedding probabilities can be approximately estimated from the stego image, we introduce the novel concept of content-selective residuals and successfully attack S-UNIWARD. We also show that this attack, which is made possible by a faulty probabilistic selection channel, can be prevented by properly adjusting the stabilizing constant in the UNIWARD distortion function.
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 ..."
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Cited by 2 (2 self)
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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. 1
