Robust Invisible Video Watermarking with Attention
By Kevin Alex Zhang, Lei Xu, Alfredo Cuesta-Infante, Kalyan Veeramachaneni. 2019
228 citations recorded by the source corpus.
The goal of video watermarking is to embed a message within a video file in a way such that it minimally impacts the viewing experience but can be recovered even if the video is redistributed and modified, allowing media producers to assert ownership over their content. This paper presents RivaGAN, a novel architecture for robust video watermarking which features a custom attention-based mechanism for embedding arbitrary data as well as two independent adversarial networks which critique the video quality and optimize for robustness. Using this technique, we are able to achieve state-of-the-art results in deep learning-based video watermarking and produce watermarked videos which have minimal visual distortion and are robust against common video processing operations.
Subfields: Computer Vision and Pattern Recognition, Safety, Robustness & Interpretability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Feng Yang
- Peyman Milanfar
- Ruohan Zhan
- Huiwen Chang
- Xiyang Luo
- Luc Van Gool
- Ming-Hsuan Yang
- Michael Elad
Citations in the corpus
- The Gradient of Generative AI Release: Methods and Considerations (2023, neutral): “Research strives ensure these watermarks are invisible to the human-eye [107], do not affect output quality [46], and tamper-proof from model attacks and alterations like fine-tuning via methods such as embedding noise as watermarks [106].”
- Distortion Agnostic Deep Watermarking (2020, background): “Deep watermarking has since been generalized to both video [39, 44] and audio [36].”