Software Watermarking: Progress and Challenges

Dey, Ayan; Bhattacharya, Swapan; Chaki, Nabendu

doi:10.1007/s41403-018-0058-8

Cited by 12 publications

(4 citation statements)

References 67 publications

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“…The table shows each attribute's maximum and average length (columns Max and Mean, respectively), using the number of characters of their values as references. On the other hand, we used the dataset Forest Cover Type (denoted as D C ) 10 to evaluate the watermark preservation in numerical data. In this case, we perform the watermark synchronization with MA-NM.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…Since this work targets the quality of the watermark and the data resulting from the query, we use metrics oriented to evaluate those aspects. Considering that the technique uses 10 The Forest Cover Type dataset is available online in the University of California Irvine (UCI) machine learning repository at http://kdd.ics.uci.edu/ databases/covertype/covertype.html [48] 11 The binary length BL denotes the number of bits used for the value binary representation. The bigger the binary length, the higher the cover for mark embedding.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…Researchers first proposed watermarking techniques to secure multimedia data. Nevertheless, over time, they have been successfully used to protect other digital assets, such as compiled code [10], relational databases [11], neural networks [12], etc. Regarding relational data, considering third parties can readily access them through the Internet, watermarks secure them without restricting access or portability.…”

Section: Introductionmentioning

confidence: 99%

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Study of the Watermark Source’s Topology Role on Relational Data Watermarking Robustness

Gort,

Olliaro,

Cortesi

2024

IEEE Access

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Accessing relational databases through web services increases risks of piracy and tampering. Watermarking guarantees data protection without constraining their access and management. Researchers traditionally compare the extracted watermark with the embedded one after simulating attacks to analyze its robustness. However, the literature has not addressed how much watermark signal is retained after querying multi-word textual attributes and how topological features of the watermark affect robustness. In this work, we fill this gap by evaluating a semantics-preserving approach. We execute different classes of queries in three data sets, including an actual database of a public administration. The results show that 99.99% of the marks can be detected when considering a watermark source with a topological factor close to or equal to 1. As valuable contributions, we formally defined the watermark source's topological factor and the corrosion degree of the watermark detection. This work offers hints to data owners to adjust detection parameters, increasing the chances of spotting the watermark despite having, as evidence, data highly damaged due to malicious operations.INDEX TERMS Ownership proof, plagiarism detection, relational data, robust watermarking, watermark topology.

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Section: A Experimental Setupmentioning

confidence: 99%

Section: A Experimental Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Study of the Watermark Source’s Topology Role on Relational Data Watermarking Robustness

Gort,

Olliaro,

Cortesi

2024

IEEE Access

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“…These attributes pose significant information integrity and preservation challenges [6]. Another challenge is the complexity of hiding innumerable types of digital information in a carrier signal over multimedia systems causing the carrier's channel's to prove their authenticity and validity during a cyber incident [7]. Furthermore, the remote evidence acquisition process itself is highly vulnerable while collecting digital evidence from different sources on the network including handling data nodes from an entry point of discovery to evidence remote data recovery, reconstruction, and verification [8,9].…”

Section: Introductionmentioning

confidence: 99%

MF-Ledger: Blockchain Hyperledger Sawtooth-Enabled Novel and Secure Multimedia Chain of Custody Forensic Investigation Architecture

et al. 2021

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Due to globalization and worldwide connectivity, multimedia data exchange has increased significantly over the Internet in the last decade. The life cycle of multimedia content is also getting more multifaceted as more people are accessing, sharing, modifying and re-using multimedia information. This poses serious challenges for the multimedia industry to provide integrity, reliability and trustworthiness for multimedia investigations against the growing cybersecurity threats. This paper bridges this gap by enabling a secure and transparent digital forensic investigations process using blockchain technology. MF-Ledger a Blockchain Hyperledger sawtooth-enabled novel, secure and efficient digital forensic investigation architecture is proposed where participating stakeholders create a private network to exchange and agree on different investigation activities before being stored on the blockchain ledger. We have created digital contracts (smart contracts) and implemented them using sequence diagrams to handle the stakeholders' secure interaction in the investigation process. The proposed architectural solution delivers robust information integrity, prevention, and preservation mechanism to permanently and immutably store the evidence (chain of custody) in a private permissioned encrypted blockchain ledger.

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