# A Survey of Intellectual Property Rights Protection in Big Data Applications +
## 4 Technical Solutions for Protecting Intellectual Property Rights
### Fingerprinting Algorithms  
The use of digital fingerprinting technology has proven to be a reliable way to protect the privacy and copyright of digital products from unauthorized distribution and to prevent adversarial attacks [45; 46; 47]. It is also used to track the user of multimedia content delivered via the cloud and its unauthorized distribution. Any design of algorithm fingerprinting used to generate these fingerprinting codes should consider the following properties: robustness, discriminability, compactness, and unpredictability [45]. For fingerprinting algorithms to work, an ensemble of feature vectors must be extracted from the source content, and these feature vectors must then be compared to a database of known features vectors associated with copyrighted content. A source pattern and a library pattern are considered identical if there are enough matches between them. Most audio, image, and video fingerprinting techniques either extract manually created features or train a neural network to extract fingerprint features [48]. A digital signature is also defined as a digital fingerprint [49]. Digital signatures are used to authenticate that a particular entity has submitted or approved a particular piece of data. Digital fingerprints are used to verify the accuracy of the data provided. However, a different signature is applied to each copy of the document. It provides the maximum level of protection that a digital signature can provide and can be used to track down intruders. [PERSON] and [PERSON] [50] developed a fingerprinting method that uses feature points for photo identification and authenticity. The authors limited the removal or addition of relatively large elements in their performance and robustness studies. Their decision process is based on the idea that the altered image regions represent a mismatch of several derived feature points. In studies such as ([PERSON] et al. [46]), robust fingerprints were created by training networks on typical distortions such as adding an edge noise or rotating the video. Such networks are resistant to predefined distortions, but they are no longer robust [48]. Due to the growing number of videos posted on social media and video platforms, localization of video clips is crucial for practical applications such as detecting copyright infringement in videos [51]. [PERSON] [45] proposed a reliable and perception-based fingerprinting method that can be used for both authentication and identification of video data. The resistance of the proposed fingerprint codes to content modification and geometric attacks makes them useful for content identification, but their sensitivity to malicious attacks makes them suitable for forgery detection and verification. A novel sequence normalization idea based on the circular shift properties of the discrete cosine and sine transforms enables this dual use (DCT and DST). [PERSON] et al. [47] presented a unique testing method that statistically compares two deep learning models--a victim model and a suspect model--to protect copyright. To assess whether a suspicious model is a replica of the victim model, a wide range of test metrics and effective test case generation algorithms are used to build a chain of evidence. The authors expect this to have several advantages, including that their proposed work is noninvasive because it works directly with the model and does not interfere with the training process. Second, quick scans of the two models and a limited selection of test cases make the procedure efficient. Finally, deep learning model copyright protection is highly resistant to model extraction and adaptive attacks, i.e., it can easily incorporate additional test metrics or test case generation methods to achieve a more secure and robust judgment [47].