Digital watermarking method using LDPC code for clipped image

Kuribayashi

et al. 2017

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SUMMARYWithin information hiding technology, digital watermarking is one of the most important technologies for copyright protection of digital content. Many digital watermarking schemes have been proposed in academia. However, these schemes are not used, because they are not practical; one reason for this is that the evaluation criteria are loosely defined. To make the evaluation more concrete and improve the practicality of digital watermarking, watermarking schemes must use common evaluation criteria. To realize such criteria, we organized the Information Hiding and its Criteria for Evaluation (IHC) Committee to create useful, globally accepted evaluation criteria for information hiding technology. The IHC Committee improves their evaluation criteria every year, and holds a competition for digital watermarking based on state-of-the-art evaluation criteria. In this paper, we describe the activities of the IHC Committee and its evaluation criteria for digital watermarking of still images, videos, and audio.

Section: Small-block Approachmentioning

confidence: 99%

“…For example, low-density parity-check (LDPC) and concatenated codes are employed in [4], [8], and convolutional code is employed in [9]. In general, the image quality of a stego-image could be improved by employing fewer watermark bits.…”

Section: Small-block Approachmentioning

confidence: 99%

Information Hiding and Its Criteria for Evaluation

Iwamura

Kuribayashi

et al. 2017

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“…Quantization index modulation (QIM) [6], [9], [10], [13], [17], [18], [26] is a blind embedding technique that is robust against compression. Specifically, since this technique quantizes coefficients of the host image according to a watermark, it has robustness against JPEG compression.…”

Section: Introductionmentioning

confidence: 99%

Image Watermarking Technique Using Embedder and Extractor Neural Networks

Hamamoto

2019

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An autoencoder has the potential ability to compress and decompress information. In this work, we consider the process of generating a stego-image from an original image and watermarks as compression, and the process of recovering the original image and watermarks from the stego-image as decompression. We propose embedder and extractor neural networks based on the autoencoder. The embedder network learns mapping from the DCT coefficients of the original image and a watermark to those of the stego-image. The extractor network learns mapping from the DCT coefficients of the stego-image to the watermark. Once the proposed neural network has been trained, the network can embed and extract the watermark into unlearned test images. We investigated the relation between the number of neurons and network performance by computer simulations and found that the trained neural network could provide high-quality stegoimages and watermarks with few errors. We also evaluated the robustness against JPEG compression and found that, when suitable parameters were used, the watermarks were extracted with an average BER lower than 0.01 and image quality over 35 dB when the quality factor Q was over 50. We also investigated how to represent the watermarks in the stego-image by our neural network. There are two possibilities: distributed representation and sparse representation. From the results of investigation into the output of the stego layer (3rd layer), we found that the distributed representation emerged at an early learning step and then sparse representation came out at a later step.

“…The marking position becomes unclear when a stegoimage is cropped, so a marker or synchronization code used to detect the marking position is also embedded in the stego-image [12]- [14]. Since there is no information about the marking position available, the position can only be searched for by brute force.…”

Section: Introductionmentioning

confidence: 99%

Image Watermarking Method Satisfying IHC by Using PEG LDPC Code

Hirata

Nozaki

2017

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SUMMARYWe propose a digital image watermarking method satisfying information hiding criteria (IHC) for robustness against JPEG compression, cropping, scaling, and rotation. When a stego-image is cropped, the marking positions of watermarks are unclear. To detect the position in a cropped stego-image, a marker or synchronization code is embedded with the watermarks in a lattice pattern. Attacks by JPEG compression, scaling, and rotation cause errors in extracted watermarks. Against such errors, the same watermarks are repeatedly embedded in several areas. The number of errors in the extracted watermarks can be reduced by using a weighted majority voting (WMV) algorithm. To correct residual errors in output of the WMV algorithm, we use a high-performance error-correcting code: a low-density parity-check (LDPC) code constructed by progressive edgegrowth (PEG). In computer simulations using the IHC ver. 4 the proposed method could a bit error rate of 0, the average PSNR was 41.136 dB, and the computational time for synchronization recovery was less than 10 seconds. The proposed method can thus provide high image quality and fast synchronization recovery.