Hiding digital watermarks using multiresolution wavelet transform
TL;DR: The proposed method for the digital watermarking is based on the wavelet transform and is robust to a variety of signal distortions, such as JPEG, image cropping, sharpening, median filtering, and incorporating attacks.
Abstract: In this paper, an image accreditation technique by embedding digital watermarks in images is proposed. The proposed method for the digital watermarking is based on the wavelet transform. This is unlike most previous work, which used a random number of a sequence of bits as a watermark and where the watermark can only be detected by comparing an experimental threshold value to determine whether a sequence of random signals is the watermark. The proposed approach embeds a watermark with visual recognizable patterns, such as binary, gray, or color image in images by modifying the frequency part of the images. In the proposed approach, an original image is decomposed into wavelet coefficients. Then, multi-energy watermarking scheme based on the qualified significant wavelet tree (QSWT) is used to achieve the robustness of the watermarking. Unlike other watermarking techniques that use a single casting energy, QSWT adopts adaptive casting energy in different resolutions. The performance of the proposed watermarking is robust to a variety of signal distortions, such as JPEG, image cropping, sharpening, median filtering, and incorporating attacks.
Citations
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08 Sep 2018
TL;DR: This work finds that neural networks can learn to use invisible perturbations to encode a rich amount of useful information, and demonstrates that adversarial training improves the visual quality of encoded images.
Abstract: Recent work has shown that deep neural networks are highly sensitive to tiny perturbations of input images, giving rise to adversarial examples Though this property is usually considered a weakness of learned models, we explore whether it can be beneficial We find that neural networks can learn to use invisible perturbations to encode a rich amount of useful information In fact, one can exploit this capability for the task of data hiding We jointly train encoder and decoder networks, where given an input message and cover image, the encoder produces a visually indistinguishable encoded image, from which the decoder can recover the original message We show that these encodings are competitive with existing data hiding algorithms, and further that they can be made robust to noise: our models learn to reconstruct hidden information in an encoded image despite the presence of Gaussian blurring, pixel-wise dropout, cropping, and JPEG compression Even though JPEG is non-differentiable, we show that a robust model can be trained using differentiable approximations Finally, we demonstrate that adversarial training improves the visual quality of encoded images
420 citations
TL;DR: An imperceptible and a robust combined DWT-DCT digital image watermarking algorithm that watermarks a given digital image using a combination of the Discrete Wavelet Transform (DWT) and thediscrete Cosine transform (DCT).
Abstract: The proliferation of digitized media due to the rapid growth of networked multimedia systems, has created an urgent need for copyright enforcement technologies that can protect copyright ownership of multimedia objects. Digital image watermarking is one such technology that has been developed to protect digital images from illegal manipulations. In particular, digital image watermarking algorithms which are based on the discrete wavelet transform have been widely recognized to be more prevalent than others. This is due to the wavelets' excellent spatial localization, frequency spread, and multi-resolution characteristics, which are similar to the theoretical models of the human visual system. In this paper, we describe an imperceptible and a robust combined DWT-DCT digital image watermarking algorithm. The algorithm watermarks a given digital image using a combination of the Discrete Wavelet Transform (DWT) and the Discrete Cosine Transform (DCT). Performance evaluation results show that combining the two transforms improved the performance of the watermarking algorithms that are based solely on the DWT transform.
319 citations
Cites background from "Hiding digital watermarks using mul..."
...The compromise adopted by many DWT-based watermarking algorithm, is to embed the watermark in the middle frequency ub-bands LHx and HLx where acceptable performance of imperceptibility and robustness could be achieved([14,15,16,17,18,19,20])....
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TL;DR: The experimental results show that the proposed blind watermarking algorithm is quite effective against JPEG compression, low-pass filtering, and Gaussian noise; the PSNR value of a watermarked image is greater than 40 dB.
Abstract: This paper proposes a blind watermarking algorithm based on the significant difference of wavelet coefficient quantization for copyright protection. Every seven nonoverlap wavelet coefficients of the host image are grouped into a block. The largest two coefficients in a block are called significant coefficients in this paper and their difference is called significant difference. We quantized the local maximum wavelet coefficient in a block by comparing the significant difference value in a block with the average significant difference value in all blocks. The maximum wavelet coefficients are so quantized that their significant difference between watermark bit 0 and watermark bit 1 exhibits a large energy difference which can be used for watermark extraction. During the extraction, an adaptive threshold value is designed to extract the watermark from the watermarked image under different attacks. We compare the adaptive threshold value to the significant difference which was quantized in a block to determine the watermark bit. The experimental results show that the proposed method is quite effective against JPEG compression, low-pass filtering, and Gaussian noise; the PSNR value of a watermarked image is greater than 40 dB.
209 citations
Cites background or methods from "Hiding digital watermarks using mul..."
...In the previous research [14], [18], [21]–[26], [30], [36], [37], the common problem is the use of blind detection to find the permutations in the order of significant coefficients to ensure the permutations which are the same as in the original order of the significant coefficients....
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...[30] proposed the watermarking method based on the qualified significant wavelet tree (QSWT)....
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TL;DR: This paper proposes a blind image watermarking algorithm based on the multiband wavelet transformation and the empirical mode decomposition that is robust against JPEG compression, Gaussian noise, salt and pepper noise, median filtering, and Con-vFilter attacks.
Abstract: In this paper, we propose a blind image watermarking algorithm based on the multiband wavelet transformation and the empirical mode decomposition. Unlike the watermark algorithms based on the traditional two-band wavelet transform, where the watermark bits are embedded directly on the wavelet coefficients, in the proposed scheme, we embed the watermark bits in the mean trend of some middle-frequency subimages in the wavelet domain. We further select appropriate dilation factor and filters in the multiband wavelet transform to achieve better performance in terms of perceptually invisibility and the robustness of the watermark. The experimental results show that the proposed blind watermarking scheme is robust against JPEG compression, Gaussian noise, salt and pepper noise, median filtering, and Con-vFilter attacks. The comparison analysis demonstrate that our scheme has better performance than the watermarking schemes reported recently.
169 citations
Cites background from "Hiding digital watermarks using mul..."
...[23] proposed a nonblind watermarking scheme based on the two-band wavelet transform and the qualified significant wavelet tree (QSWT), which is robust to JPEG compression, image cropping, median filter etc....
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References
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TL;DR: It is argued that insertion of a watermark under this regime makes the watermark robust to signal processing operations and common geometric transformations provided that the original image is available and that it can be successfully registered against the transformed watermarked image.
Abstract: This paper presents a secure (tamper-resistant) algorithm for watermarking images, and a methodology for digital watermarking that may be generalized to audio, video, and multimedia data. We advocate that a watermark should be constructed as an independent and identically distributed (i.i.d.) Gaussian random vector that is imperceptibly inserted in a spread-spectrum-like fashion into the perceptually most significant spectral components of the data. We argue that insertion of a watermark under this regime makes the watermark robust to signal processing operations (such as lossy compression, filtering, digital-analog and analog-digital conversion, requantization, etc.), and common geometric transformations (such as cropping, scaling, translation, and rotation) provided that the original image is available and that it can be successfully registered against the transformed watermarked image. In these cases, the watermark detector unambiguously identifies the owner. Further, the use of Gaussian noise, ensures strong resilience to multiple-document, or collusional, attacks. Experimental results are provided to support these claims, along with an exposition of pending open problems.
6,194 citations
"Hiding digital watermarks using mul..." refers background or methods in this paper
...[7] used the spread spectrum communication for multimedia watermarking....
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...In most previously proposed wavelet-based watermarking techniques [7], [10]–[12], [17]–[21], the watermark is a random sequence of bits, and can only be detected by employing detection theory....
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...As mentioned in Section I, in most previous wavelet-based approaches, for example, [7], [10]–[12], and [17]–[21], the watermark is a random sequence of bits, and can only be detected...
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TL;DR: The image coding results, calculated from actual file sizes and images reconstructed by the decoding algorithm, are either comparable to or surpass previous results obtained through much more sophisticated and computationally complex methods.
Abstract: Embedded zerotree wavelet (EZW) coding, introduced by Shapiro (see IEEE Trans. Signal Processing, vol.41, no.12, p.3445, 1993), is a very effective and computationally simple technique for image compression. We offer an alternative explanation of the principles of its operation, so that the reasons for its excellent performance can be better understood. These principles are partial ordering by magnitude with a set partitioning sorting algorithm, ordered bit plane transmission, and exploitation of self-similarity across different scales of an image wavelet transform. Moreover, we present a new and different implementation based on set partitioning in hierarchical trees (SPIHT), which provides even better performance than our previously reported extension of EZW that surpassed the performance of the original EZW. The image coding results, calculated from actual file sizes and images reconstructed by the decoding algorithm, are either comparable to or surpass previous results obtained through much more sophisticated and computationally complex methods. In addition, the new coding and decoding procedures are extremely fast, and they can be made even faster, with only small loss in performance, by omitting entropy coding of the bit stream by the arithmetic code.
5,890 citations
TL;DR: The embedded zerotree wavelet algorithm (EZW) is a simple, yet remarkably effective, image compression algorithm, having the property that the bits in the bit stream are generated in order of importance, yielding a fully embedded code.
Abstract: The embedded zerotree wavelet algorithm (EZW) is a simple, yet remarkably effective, image compression algorithm, having the property that the bits in the bit stream are generated in order of importance, yielding a fully embedded code The embedded code represents a sequence of binary decisions that distinguish an image from the "null" image Using an embedded coding algorithm, an encoder can terminate the encoding at any point thereby allowing a target rate or target distortion metric to be met exactly Also, given a bit stream, the decoder can cease decoding at any point in the bit stream and still produce exactly the same image that would have been encoded at the bit rate corresponding to the truncated bit stream In addition to producing a fully embedded bit stream, the EZW consistently produces compression results that are competitive with virtually all known compression algorithms on standard test images Yet this performance is achieved with a technique that requires absolutely no training, no pre-stored tables or codebooks, and requires no prior knowledge of the image source The EZW algorithm is based on four key concepts: (1) a discrete wavelet transform or hierarchical subband decomposition, (2) prediction of the absence of significant information across scales by exploiting the self-similarity inherent in images, (3) entropy-coded successive-approximation quantization, and (4) universal lossless data compression which is achieved via adaptive arithmetic coding >
5,559 citations
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TL;DR: The paper discusses the feasibility of coding an "undetectable" digital water mark on a standard 512/spl times/512 intensity image with an 8 bit gray scale, capable of carrying such information as authentication or authorisation codes, or a legend essential for image interpretation.
Abstract: The paper discusses the feasibility of coding an "undetectable" digital water mark on a standard 512/spl times/512 intensity image with an 8 bit gray scale. The watermark is capable of carrying such information as authentication or authorisation codes, or a legend essential for image interpretation. This capability is envisaged to find application in image tagging, copyright enforcement, counterfeit protection, and controlled access. Two methods of implementation are discussed. The first is based on bit plane manipulation of the LSB, which offers easy and rapid decoding. The second method utilises linear addition of the water mark to the image data, and is more difficult to decode, offering inherent security. This linearity property also allows some image processing, such as averaging, to take place on the image, without corrupting the water mark beyond recovery. Either method is potentially compatible with JPEG and MPEG processing. >
1,407 citations
TL;DR: This work explores both traditional and novel techniques for addressing the data hiding process and evaluates these techniques in light of three applications: copyright protecting, tamper-proofing, and augmentation data embedding.
Abstract: Data hiding is the process of embedding data into image and audio signals. The process is constrained by the quantity of data, the need for invariance of the data under conditions where the `host' signal is subject to distortions, e.g., compression, and the degree to which the data must be immune to interception, modification, or removal. We explore both traditional and novel techniques for addressing the data hiding process and evaluate these techniques in light of three applications: copyright protecting, tamper-proofing, and augmentation data embedding.
1,343 citations
"Hiding digital watermarks using mul..." refers background in this paper
...A variety of improvements were proposed against image compression and filtering [2]–[6]....
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