Adapting Image Watermarking Techniques for Video Using Deep Learning

Abstract

The rapid growth of digital media has underscored the necessity of protecting intellectual property    through    effective    watermarking[18].    Although    image    watermarking    is well-researched,  its  application  to  video  presents unique challenges due to the added complexity of temporal data and increased volume [1]. This paper introduces an innovative approach for transitioning from image to video watermarking by employing deep learning techniques [5]. The proposed framework leverages convolutional neural networks (CNNs) for spatial  watermark  embedding  and  recurrent  neural  networks  (RNNs),  such  as  Long Short-Term Memory (LSTM) networks, for maintaining temporal consistency, ensuring that watermarks are both imperceptible and resilient to common distortions and attacks [20].It acknowledges the challenges in extending image watermarking to video due to temporal data complexities and proposes a deep learning-based solution [9]. This approach uses CNNs for spatial embedding and RNNs, like LSTMs, for maintaining temporal consistency, ensuring that the watermarks are imperceptible yet resilient to common video distortions[2].The method ensures that watermarks are imperceptible (unnoticeable to viewers) and resilient (able to withstand distortions and attacks) [10]. This dual  focus on imperceptibility and resilience is crucial for effective watermarking in dynamic video content[12].