Brazil

A digital watermark is an information or signal inserted into a digital content (image, audio, video, document, software) that can be used to determine its ownership, showing who created or sold it, or to ensure its integrity. Typically, the same watermark is added consistently to mark movie collections. Consequently, media with a visible watermark will be eventually susceptible to the necessity of watermark remission, operation that could be an issue. This paper presents two new and very
fast algorithms for watermark detection and removal. The here considered watermarks could be blended, overlaid or added in a continuous form to the digital video on remission treatment. The first of such algorithms use a template matching approach to pre-process the blended watermarks frame-to-frame in the Hue-Saturation-Value (HSV) color space, and then improve the detection using a post-processing step. The second algorithm presents two steps as well. Initially, it estimates the structure of a translucent watermark observing the constancy of its gradient magnitude in the area corresponding to the tenth part of each video. Secondly, it uses a sliding window to detect the watermarked in the frames. The results show that watermark detection by both algorithms has high sensitivity. Moreover, they are very fast when compared with other methods especially those based on deep learning

IWSSIP 2020

Watermark Detection and Clearance in Video Using Simple Signal and Image Processing Techniques

sliding windows

digital signal

watermark

gradient

image processing

steganography

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

The 27th International Conference on Systems, Signals and Image Processing.

Interpretability of machine learning models: application for lawsuit prediction in the energy sector

Robust Scene Understanding Via Real-Time Approximate Bayesian Computations

Modern Architecture for Core Video Understanding

Meta-Learning Applications in Digital Image Processing

Learning to "see" touch

Biomedical Signal Processing and Analysis I

Biomedical Signal Processing and Analysis II

Signal Processing

Machine Learning & Prediction

Image-Based Detection, Recognition and Classification I

keynote

live discussion

https://d3smihljt9218e.cloudfront.net/transcript/55956/subtitle/9c226e3576aa9699d60b3ec00bb54a59.vtt

English (automatic)

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Watermark Detection and Clearance in Video Using Simple Signal and Image Processing Techniques

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Interpretability of machine learning models: application for lawsuit prediction in the energy sector

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