Countermeasures to Attacks

Johnson, Neil F.; Duric, Zoran; Jajodia, Sushil

doi:10.1007/978-1-4615-4375-6_4

Cited by 10 publications

(8 citation statements)

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“…In our steganography problem, a randomized code is used to satisfy the perfect-undetectability condition of (2). Without the secret key, a deterministic code generally could not satisfy the perfect-undetectability condition.…”

Section: Secret Keymentioning

confidence: 99%

Perfectly Secure Steganography: Capacity, Error Exponents, and Code Constructions

Wang

Moulin²

2008

IEEE Trans. Inform. Theory

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An analysis of steganographic systems subject to the following perfect undetectability condition is presented in this paper. Following embedding of the message into the covertext, the resulting stegotext is required to have exactly the same probability distribution as the covertext. Then no statistical test can reliably detect the presence of the hidden message. We refer to such steganographic schemes as perfectly secure. A few such schemes have been proposed in recent literature, but they have vanishing rate. We prove that communication performance can potentially be vastly improved; specifically, our basic setup assumes independently and identically distributed (i.i.d.) covertext, and we construct perfectly secure steganographic codes from public watermarking codes using binning methods and randomized permutations of the code. The permutation is a secret key shared between encoder and decoder. We derive (positive) capacity and random-coding exponents for perfectly-secure steganographic systems. The error exponents provide estimates of the code length required to achieve a target low error probability.In some applications, steganographic communication may be disrupted by an active warden, modelled here by a compound discrete memoryless channel. The transmitter and warden are subject to distortion constraints. We address the potential loss in communication performance due to the perfect-security requirement. This loss is the same as the loss obtained under a weaker order-1 steganographic requirement that would just require matching of first-order marginals of the covertext and stegotext distributions. Furthermore, no loss occurs if the covertext distribution is uniform and the distortion metric is cyclically symmetric; steganographic capacity is then achieved by randomized linear codes. Our framework may also be useful for developing computationally secure steganographic systems that have near-optimal communication performance.

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Section: Secret Keymentioning

confidence: 99%

Perfectly Secure Steganography: Capacity, Error Exponents, and Code Constructions

Wang

Moulin²

2008

IEEE Trans. Inform. Theory

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“…A signature of an embedding algorithm is a distinctive characteristic of some feature or collection of features of an image that can be used to identify the existence of hidden data in the image. Signatures include statistical measures or properties that may give different results for an original image than a stego image (an image that has data hidden in it), such as the image histogram does for the jsteg embedding algorithm [17]. Color palettes in stego images can also be used as signatures, such as for S-Tools [16].…”

Section: Steganalysis and Previous Workmentioning

confidence: 99%

An artificial neural network for wavelet steganalysis

2005

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Hiding messages in image data, called steganography, is used for both legal and illicit purposes. The detection of hidden messages in image data stored on websites and computers, called steganalysis, is of prime importance to cyber forensics personnel. Automating the detection of hidden messages is a requirement, since the shear amount of image data stored on computers or websites makes it impossible for a person to investigate each image separately. This paper describes research on a prototype software system that automatically classifies an image as having hidden information or not, using a sophisticated artificial neural network (ANN) system. An ANN software package, the ISU ACL NetWorks Toolkit, is trained on a selection of image features that distinguish between stego and nonstego images. The novelty of this ANN is that it is a blind classifier that gives more accurate results than previous systems. It can detect messages hidden using a variety of different types of embedding algorithms. A Graphical User Interface (GUI) combines the ANN, feature selection, and embedding algorithms into a prototype software package that is not currently available to the cyber forensics community. ABSTRACTHiding messages in image data, called steganography, is used for both legal and illicit purposes. The detection of hidden messages in image data stored on websites and computers, called steganalysis, is of prime importance to cyber forensics personnel. Automating the detection of hidden messages is a requirement, since the shear amount of image data stored on computers or websites makes it impossible for a person to investigate each image separately. This paper describes research on a prototype software system that automatically classifies an image as having hidden information or not, using a sophisticated artificial neural network (ANN) system. An ANN software package, the ISU ACL NetWorks Toolkit, is trained on a selection of image features that distinguish between stego and nonstego images. The novelty of this ANN is that it is a blind classifier that gives more accurate results than previous systems. It can detect messages hidden using a variety of different types of embedding algorithms. A Graphical User Interface (GUI) combines the ANN, feature selection, and embedding algorithms into a prototype software package that is not currently available to the cyber forensics community.

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“…To do this, the fast Fourier transforms of and each of the primitive patterns (which have also been filtered with ) are calculated. 3 The complex conjugate of each frequency-domain primitive pattern is then pixel-wise multiplied with the frequency-domain folded image. Then, the inverse fast Fourier transform of the product is applied.…”

Section: B Detector: Definition and Constraintsmentioning

confidence: 99%

Hardware implementation perspectives of digital video watermarking algorithms

Mathai

Kundur

Sheikholeslami

2003

IEEE Trans. Signal Process.

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Abstract-We consider hardware implementation aspects of the digital watermarking problem through the implementation of a well-known video watermarking algorithm called Just Another Watermarking System (JAWS); we discuss the time and area constraints that must be satisfied by a successful hardware implementation. A hardware architecture that implements the algorithm under the constraints is then proposed. The architecture is analyzed to gain an understanding of the relationships between algorithmic features and implementation cost. Some general findings of this work that can be applied toward making algorithmic developments more amenable to hardware implementation are finally presented.

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Countermeasures to Attacks

Cited by 10 publications

References 0 publications

Perfectly Secure Steganography: Capacity, Error Exponents, and Code Constructions

Perfectly Secure Steganography: Capacity, Error Exponents, and Code Constructions

An artificial neural network for wavelet steganalysis

Hardware implementation perspectives of digital video watermarking algorithms

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