Digital Watermarking Techniques for Data Leakage Traceability and Protection

ChatGPT raises new plagiarism and copyright concerns, prompting the exploration of digital watermarking as an invisible, computer‑detectable solution; OpenAI even considers embedding watermarks in its models to mitigate misuse.

The technology dates back to the late 1990s, with early research in the United States and the United Kingdom leading to widespread adoption for intellectual‑property protection, military confidentiality, anti‑counterfeiting, and network security.

Data leakage has exploded, with 2020 alone seeing 3.6 billion compromised records—driven by system failures, human error, and malicious attacks—and has spawned a black‑market chain involving data thieves, brokers, and buyers.

Given this landscape, protecting data across its entire lifecycle is critical, and the talk focuses on watermarking during the data‑exchange phase for traceability and copyright enforcement.

Traditional visible watermarks are easily removed using simple image‑processing or AI‑based attacks, highlighting the need for robust, invisible (dark) watermarks.

A generic digital‑watermark framework consists of two stages: embedding the watermark into the host data using a secret key, and later extracting it to verify provenance.

Watermark quality is judged by five metrics: imperceptibility, capacity, robustness, practicality, and security.

Image watermarks: LSB methods, transform‑domain techniques (DCT, DWT), and their trade‑offs.

Text watermarks: typographic tweaks, zero‑width characters, spacing variations, and natural‑language synonym or syntactic changes.

Database watermarks: reversible schemes for numeric and character fields, pre‑processing, embedding, and extraction steps.

In e‑commerce, watermarking can address four key leakage scenarios—screenshots, bulk export, printed documents, and unstructured files—by combining visible prompts with hidden identifiers, applying text watermarks to sensitive fields, and using database watermarks for bulk data.

The proposed database‑watermark solution embeds identifiers (user ID, timestamp, system) into sensitive attributes, inserts watermarks across all rows, employs multiple embedding strategies, and adds error‑correction for tamper detection.

Open challenges include preventing generic watermark removal, protecting ultra‑short sensitive strings, and optimizing computational and storage overhead; future research should address these gaps.

Effective leakage tracing answers three questions: source, path, and recipient, enabling containment, system hardening, legal action, and user‑focused risk mitigation.