Visible watermarks inpainting using deep learning for multimedia application

N. Gapon; E. Yadrets; V. Voronin; A. Sosnovsky; M. Zhdanova; E. Semenishchev

doi:10.1117/12.2690884

27 November 2023 Visible watermarks inpainting using deep learning for multimedia application

N. Gapon, E. Yadrets, V. Voronin, A. Sosnovsky, M. Zhdanova, E. Semenishchev

Proceedings Volume 12767, Optoelectronic Imaging and Multimedia Technology X; 127671U (2023) https://doi.org/10.1117/12.2690884
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Conference Poster

Abstract

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.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

N. Gapon, E. Yadrets, V. Voronin, A. Sosnovsky, M. Zhdanova, and E. Semenishchev "Visible watermarks inpainting using deep learning for multimedia application", Proc. SPIE 12767, Optoelectronic Imaging and Multimedia Technology X, 127671U (27 November 2023); https://doi.org/10.1117/12.2690884

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