An evaluation method to assess the robustness of invisible watermarking implementations in unstructured digital content, including images, video, audio, documents, and webpages, is described in this standard. Also described are distortion inferences that may result in the compromise of the watermark information embedded in digital content. Furthermore, the metrics used for evaluation and test cases for assessing the robustness against different distortion inferences are specified. This standard does not specify the evaluation for the robustness of the watermarking implementations in structured content, for example, a database. The risks faced with watermarking implementations in structured content are different from those of unstructured content and need different test cases.
- Standard Committee
- C/CPSC - Cybersecurity and Privacy Standards Committee
- Status
- Active Standard
- PAR Approval
- 2023-03-30
- Board Approval
- 2025-05-28
- History
-
- Published:
- 2025-08-08
Working Group Details
- Society
- IEEE Computer Society
- Standard Committee
- C/CPSC - Cybersecurity and Privacy Standards Committee
- Working Group
-
DLTWG - Data Leakage Tracing Working Group
- IEEE Program Manager
- Malia Zaman
Contact Malia Zaman - Working Group Chair
- Shiqi Li
Other Activities From This Working Group
Current projects that have been authorized by the IEEE SA Standards Board to develop a standard.
P3477
Standard for Evaluation Method for Anonymization Effectiveness in Data Sharing
This standard specifies an evaluation methodology to assess the re-identification risk following the application of anonymization processes to data intended for sharing. Specifically, the elements contributing to re-identification risks are described, which encompass threats to processed data, security measures implemented for the processed data, and potential impacts on the data subject. Furthermore, this standard provides a method for evaluating the levels of re-identification risk based on these considerations, and a comprehensive evaluation procedure. The application of the methodology across common data sharing scenarios, such as internal data sharing, external data sharing, and data publishing, is also described.
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