Adaptive Flexible 3D Histogram Watermarking

Abstract

Watermarking technology has attracted increasing attentions in the past few years, and a great deal traditional and deep learning-based methods have been proposed. However, these methods usually suffer from the following three challenges: First, the current algorithms are designed separately for images or videos, and there is no universal solution. Second, most algorithms cannot resist screen recording, which limits its application. Third, some algorithms can only embed fixedlength watermarks and cannot handle the embedding capacity flexibly. In this paper, a novel watermarking scheme is proposed based on spatial and temporal histograms, in which two types of histogram watermarking are designed: One is constructed in the spatial domain, using the low-frequency characteristics of the image to change the shape of the histogram and embed the watermark. The other is established in the time domain, which uses the similarity of adjacent frames and combines texture features to modify the shape of the temporal histogram to embed the watermark. Experimental results compared with the state-of-the-art demonstrate that the proposed scheme achieves superior performance.

Spatio-Temporal Context Based Adaptive Camcorder Recording Watermarking

“…Video watermarking is such a technique, which embeds hidden data with minimal impact on the visual appearance of the video. Recently, it has been extensively studied and applied to many realistic scenarios, including but not limited to copyright protection [18], content authentication [3] and information retrieving [12]. In this paper, we aim to solve such a kind of problem, in which the pirate secretly records a video displayed on a screen with a smartphone in a handheld manner and distributes it illegally.…”