An Improved U-Net for Watermark Removal

Fu, Lijun; Shi, Bei; Sun, Lei; Zeng, Jiawen; Chen, Deyun; Zhao, Hongwei; Tian, Chunwei

doi:10.3390/electronics11223760

Cited by 10 publications

(2 citation statements)

References 60 publications

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“…The binary image was embedded into the Lena, baboon, airplane, splash and girl images using the proposed algorithm. The proposed work was resistant against histogram equalization and JPEG compression attacks [16][17][18][19].…”

Section: Related Workmentioning

confidence: 99%

A Semi-Fragile, Inner-Outer Block-Based Watermarking Method Using Scrambling and Frequency Domain Algorithms

et al. 2023

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Image watermarking is most often used to prove that an image belongs to someone and to make sure that the image is the same as was originally produced. The type of watermarking used for the detection of originality and tampering is known as authentication-type watermarking. In this paper, a blind semi-fragile authentication watermarking method is introduced. Although the main concern in this paper is authenticating the image, watermarking for proving ownership is additionally implemented. The method considers the image as two main parts: an inner part and an outer part. The inner and outer parts are divided into non-overlapping blocks. The block size of the inner and outer part are different. The outer blocks have a greater area than the inner blocks so that their watermark-holding capacity is greater, providing enough robustness for semi-fragility. The method is semi-fragile and the watermarked image is authenticated despite the JPEG being compressed to 75% quality. The embedded watermark also survives innocent types of image operations, such as intensity adjustment, histogram equalization and gamma correction. Semi-fragile and selectively fragile authentication is valuable and in high demand specifically because it survives these innocent image operations while detecting ill-intentioned tampering. In this work, we embed a binary watermark into the inner and outer parts of images using a scrambling algorithm, discrete wavelet transform (DWT) and discrete cosine transform (DCT) in the blocks. The proposed methodology has high image quality after watermarking, with a PSNR value of 40.577 , and high quality is also achieved after JPEG compression. The embedding process provides acceptable image quality after tamper attacks, including JPEG compression, Gaussian noise, average filtering, and scaling attacks with PSNR values greater than 29. Experimental results obtained show that the proposed semi-fragile watermarking algorithm is more robust, secure and resistant than other algorithms in the literature.

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Section: Related Workmentioning

confidence: 99%

A Semi-Fragile, Inner-Outer Block-Based Watermarking Method Using Scrambling and Frequency Domain Algorithms

et al. 2023

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“…One approach is to use convolutional neural networks (CNNs) specifically designed for image processing. For example, CNN can identify texture features and edges, which helps identify regions containing redundant objects [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Visible watermarks inpainting using deep learning for multimedia application

Gapon,

Yadrets,

Voronin

et al. 2023

Optoelectronic Imaging and Multimedia Technology X

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In recent decades, with the development of digital technologies and the spread of the Internet, photography has become an integral part of our daily lives. Often, images with objects superimposed in text, symbols, or drawings. These superimposed objects can serve a variety of purposes. Adding text or graphics can highlight specific details in an image or convey important information, such as the event's date, location, or specifics. Adding graphic elements may be used commercially to promote products, services, or a brand. Another area of application of such objects is the field of intellectual property protection or verification of the authenticity of a digital image. The problem with such images is that objects can completely overlap the image, leading to a distortion of the perceived information. In this article, we propose an effective convolutional autoencoder model for removing watermarking and restoring an image's usable part. This network can perfectly remove the embedded, visible watermark, restoring the image. Experimental results demonstrate the effectiveness and superiority of our scheme.

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Enhanced diffusion-based model for rubber stamp removal

Cui,

Yang,

2024

Multimed Tools Appl

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An Improved U-Net for Watermark Removal

Cited by 10 publications

References 60 publications

A Semi-Fragile, Inner-Outer Block-Based Watermarking Method Using Scrambling and Frequency Domain Algorithms

A Semi-Fragile, Inner-Outer Block-Based Watermarking Method Using Scrambling and Frequency Domain Algorithms

Visible watermarks inpainting using deep learning for multimedia application

Enhanced diffusion-based model for rubber stamp removal

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