Patent classifications
H04L2209/608
NFT production from feature films including spoken lines
Methods and processes for manufacture of an image product from a digital image. An object in the digital image is detected and recognized. Object metadata is assigned to the object, the object metadata linking sound to the object in the digital image which produced the sound. At least one cryptographic hash of the object metadata is generated, and the hash is written to a node of a transaction processing network.
Secure content augmentation systems and methods
The disclosure relates to, among other things, systems and methods for augmenting and/or otherwise supplementing content using watermarks. Consistent with embodiments disclosed herein, a user device such as a smartphone may be used to retrieve watermark information encoded in a watermark. The watermark information may comprise content that supplements an associated content item, link and/or location information that may be used to retrieve supplemental content, and/or the like. In some embodiments, the watermark information may comprise cryptographic and/or other access token information used to decrypt and/or otherwise access supplemental content.
Decryption and variant processing
A plurality of byte ranges forms a sample for content output from a player device, and includes at least one double-encrypted byte range. The plurality of byte ranges is stored in a secured memory, and the at least one double-encrypted byte range is partially decrypted to generate at least one decrypted singe-encrypted byte range. The plurality of byte ranges is stored in an unsecured memory using the at least one decrypted single-encrypted byte range in place of the at least one double-encrypted byte range.
Content anti-piracy management system and method
A software and/or hardware facility that can be used by content owners to assert ownership of content so that copyright friendly websites and services can take action against copyright piracy effectively, efficiently and is scalable is disclosed. The facility makes available to all content owners watermarking/fingerprinting technology so an identifier (e.g., a unique code) can be embedded in the content (e.g., video/audio portion of each video content asset). The facility utilizes blockchain technology to add information related to each unique identifier in a database and allows an authorized user (e.g., the owner) to update the information through a blockchain transaction.
Image distribution using composite re-encrypted images
Some embodiments enable distributing data (e.g., recorded video, photographs, recorded audio, etc.) to a plurality of users in a manner which preserves the privacy of the respective users. Some embodiments leverage homomorphic encryption and proxy re-encryption techniques to manipulate the respective data so that selected portions of it are revealed according to an identity of the user currently accessing the respective data.
Derivation of film libraries into NFTs based on image frames
Methods and processes for manufacture of an image product from a digital image. An object in the digital image is detected and recognized. Object metadata is assigned to the object, the object metadata linking sound to the object in the digital image which produced the sound. At least one cryptographic hash of the object metadata is generated, and the hash is written to a node of a transaction processing network.
SYSTEM AND METHOD FOR WATERMARKING A MACHINE LEARNING MODEL
Exemplary systems and methods are directed to embedding data into a machine learning model. A processing device executes program code for running a machine learning model, which has a plurality of parameter values. The processing device receives a message to be embedded into the machine learning model. The message is encrypted according to a set of keys of a cryptographic algorithm. The encrypted message is converted to a corresponding binary representation. The binary representation of the encrypted message is embedded into at least one of the one or more parameters of the machine learning model. The embedding operation modifies the at least one parameter value of the machine learning model.
Blockchain digital rights management streaming library
A computer-implemented method includes: receiving, by a computer device, an artifact and a first token with a check-in request; applying, by the computer device, a first level fragile watermark to the artifact, wherein the first level fragile watermark includes ownership information from the first token; receiving, by the computer device, a second token with a check-out request; applying, by the computer device, a second level fragile watermark to a copy of the first level fragile watermarked artifact, wherein the second level fragile watermark includes authentication information from the second token; and transmitting, by the computer device, the second level fragile watermarked copy of the artifact to a client device.
NFT INVENTORY PRODUCTION INCLUDING METADATA ABOUT A REPRESENTED GEOGRAPHIC LOCATION
Methods and processes for manufacture of an image product from a digital image. An object in the digital image is detected and recognized. Object metadata is assigned to the object, the object metadata linking sound to the object in the digital image which produced the sound. At least one cryptographic hash of the object metadata is generated, and the hash is written to a node of a transaction processing network.
THIRD PARTY BASED PIRATED COPY TRACING
According to implementations of the subject matter described herein, a solution is provided for pirated copy tracing based on a third party. In the solution, a report on a pirated copy of a digital content is received from a third party, wherein the report comprises first secret information for characterizing a first identification, time information and tracing information of the pirated copy. Subsequently, a request for verifying the report is received to determine whether the report is valid. When the report is determined as valid, a licensee associated with the report is marked as a first status to indicate that the pirated copy might be leaked by the licensee. Therefore, the pirated copy may be effectively traced based on third parties. The tracing information in the report can be hidden, and other third parties can therefore be prevented from using the tracing information for duplicate reports.