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Lecture notes for undergraduate and first-year graduate students on digital watermarking and data embedding in multimedia data.

Based on lectures developed at University of Maryland, College Park, USA.

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Today, 3D objects are an increasingly popular form of media. It has become necessary to secure them during their transmission or archiving. In this paper, we propose a two tier reversible data hiding method for 3D objects in the encrypted domain. Based on the homomorphic properties of the Paillier cryptosystem, our proposed method embeds a first tier message in the encrypted domain which can be extracted in either the encrypted domain or the clear domain. Indeed, our method produces a marked 3D object which is visually very similar to the original object.

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Nowadays a steganography has to face challenges to both feature-based staganalysis and convolutional neural network (CNN) based steganalysis. In this paper, we present a novel steganographic scheme to incorporate synchronizing modification directions and iterative adversarial perturbations to enhance steganographic performance. Firstly an existing steganographic function is employed to compute initial costs. Then the secret message bits are embedded following clustering modification directions profile.

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Unlike most existing steganography methods which are main- ly focused on designing embedding cost, in this paper, we propose a new method to enhance existing steganographic methods via stego generation and selection. The proposed method firstly trains a steganalytic network according to the steganography to be enhanced, and then tries to adjust a tiny part of original embedding costs based on the magnitudes of it and the corresponding gradients obtained from the pre-trained network, and generates many candidate stegos in a random manner.

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Steganography is the art and science of hiding data within innocent-looking objects (cover objects). Multimedia objects such as images and videos are an attractive type of cover objects due to their high embedding rates. There exist many techniques for performing steganography in both the literature and the practical world. Meanwhile, the definition of the steganographic capacity for multimedia and how to be calculated has not taken full attention.

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Audio steganography is a technology that embeds messages into audio without raising any suspicion from hearing it. Current steganography methods are based on heuristic cost designs. In this work, we proposed a framework based on Generative Adversarial Network (GAN) to approach optimal embedding for audio steganography in the temporal domain. This is the first attempt to approach optimal embedding with GAN and automatically learn the embedding probability/cost for audio steganography.

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Many portable imaging devices use the operation of “trunc” (rounding towards zero) instead of rounding as the final quantizer for computing DCT coefficients during JPEG compression. We show that this has rather profound consequences for steganography and its detection. In particular, side-informed steganography needs to be redesigned due to the different nature of the rounding error. The steganographic algorithm J-UNIWARD becomes vulnerable to steganalysis with the JPEG rich model and needs to be adjusted for this source.

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The widespread application of audio communication technologies has speeded up audio data flowing across the Internet, which made it an popular carrier for covert communication. In this paper, we present a cross-modal steganography method for hiding image content into audio carriers while preserving the perceptual fidelity of the cover audio.

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Embedding costs used in content-adaptive image steganographic schemes can be defined in a heuristic way or with a statistical model. Inspired by previous steganographic methods, i.e., MG (multivariate Gaussian model) and MiPOD (minimizing the power of optimal detector), we propose a model-driven scheme in this paper. Firstly, we model image residuals obtained by high-pass filtering with quantized multivariate Gaussian distribution. Then, we derive the approximated Fisher Information (FI). We show that FI is related to both Gaussian variance and filter coefficients.

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