Dataset Watermarking with Sub-Message Embedding for Robust Recovery
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Solution Overview
Problem
Existing dataset watermarking methods face limitations in embedding long messages without distorting data content, leading to increased error rates and reduced robustness against attacks, with capacity varying based on data type and application.
Innovation Solution
A dataset watermarking framework that encodes original messages into multiple sub-messages, employing various techniques like text, audio, and video watermarking, and uses neural networks for encoding and decoding to reconstruct the original message from sub-messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If longer watermarking messages are embedded into data samples, then the amount of information that can be embedded increases, but the distortion of data content increases and robustness against attacks decreases
Solution Approach 1:
The patent divides the original watermark message into multiple shorter sub-messages that are distributed across different data samples. This segmentation allows the system to embed more total information while maintaining robustness, as each individual sub-message is shorter and less susceptible to attacks, yet collectively they convey the complete original message.
Solution Approach 2:
The patent transitions from embedding a single long message into individual data samples to distributing multiple shorter sub-messages across multiple data samples. This dimensional shift from one-dimensional embedding to multi-dimensional distribution enables increased information capacity without proportionally increasing distortion or reducing robustness.
2Quantity of substance
If longer watermarking messages are embedded into data samples, then the amount of information that can be embedded increases, but the distortion of data content increases
Solution Approach 1:
By segmenting the long watermark message into multiple shorter sub-messages and distributing them across different data samples, the patent reduces the distortion impact on any single data sample. Each sample only contains a small portion of the watermark information, minimizing the distortion introduced into the data content while still enabling embedding of the complete original message across the dataset.
Solution Approach 2:
The patent moves from concentrating all watermark information into individual data samples to distributing it across multiple samples in the dataset. This dimensional redistribution allows the system to embed longer total messages while keeping the distortion per sample low, as the information is spread across multiple dimensions rather than concentrated in one.
3Quantity of substance
If longer watermarking messages are embedded into data samples, then the amount of information that can be embedded increases, but the error rate in extracted messages increases
Solution Approach 1:
The patent segments the watermark message into multiple shorter sub-messages that are embedded in different data samples. During extraction, the system collects these shorter sub-messages from multiple samples and reconstructs the original message. This segmentation reduces the error rate because each individual extraction operation deals with shorter, more reliable sub-messages rather than attempting to extract a single long message that is more prone to errors.
Solution Approach 2:
The patent transforms the extraction process from a single-dimensional extraction of one long message to a multi-dimensional collection and reconstruction of multiple shorter sub-messages. This dimensional approach allows the system to aggregate information from multiple reliable sources (data samples), thereby reducing the overall error rate in the extracted message while enabling embedding of longer total information.
4Manufacturing precision
If watermarking techniques avoid noticeable distortions in data samples, then data quality is preserved, but the capacity to embed long messages is reduced
Solution Approach 1:
The patent segments the watermark embedding task across multiple data samples, with each sample containing only a short sub-message. This segmentation allows the system to preserve data quality in each individual sample (since the distortion from embedding a short sub-message is minimal) while still achieving high overall message capacity by aggregating the sub-messages across all samples in the dataset.
Solution Approach 2:
The patent transitions from embedding long messages into individual samples (which would cause noticeable distortion) to embedding multiple short sub-messages across multiple samples. This dimensional shift enables the system to maintain high data quality preservation in each sample while achieving large total message capacity through the collective information stored across all samples.
Data Source
AI summary
Systems and methods for watermarking objects of a digital dataset are disclosed. The method includes, at a first moment in time, acquiring a watermark message of a first size and generating a plurality of sub-messages based on the watermark message. The plurality of sub-messages are indicative of encoded portions of the watermark message. Each of the plurality of sub-messages is different from the watermark message and having a second size, the second size being smaller than the first size. The method also includes generating a watermarked object by embedding a given object from the digital dataset with a given one of the plurality of sub-messages, in lieu of the watermark message.