Techniques for managing activation of functionalities in an information processing system are provided. For example, a method generates, at a first node in a distributed ledger network, at least one data object comprising data associated with a system and one or more unactivated functionalities of the system, and the data object further comprises one or more parameter fields configured for one or more other nodes in the distributed ledger network to subsequently insert data therein. The method obtains the at least one data object after data is inserted in the one or more parameter fields by the one or more other nodes. The method sends the at least one object to an additional node in the distributed ledger network for use in activating the one or more unactivated functionalities of the system.

The present disclosure provides systems and methods for proving an event. The system comprises a network of one or more beacon nodes and each beacon node is configured to: receive a request over the network for proving a time and location for an event, and the request comprises event data generated by a requesting entity; create a timestamp for the event using a clock of the beacon node, wherein the clock is synchronized to a trusted time source; generate a hash code for the timestamp and the event data and record the hash code to a ledger at the beacon node.

Systems, methods, and program products for managing digital production from one or more production devices with one or more sources providing inputs of production designs and/or production options are disclosed.

This disclosure aims to overcome at least some of the drawbacks associated with today's content sharing applications. In one aspect, this disclosure enables decentralized sharing of content based on crypto protocols and distributed database technology (e.g., blockchain technology). In one aspect, the content to be shared is encrypted and included in a session document that is stored in a distributed database that can be shared without requiring a central administrator.

Systems, methods, and computer program products for determining an attack on a neural network. A data sample is received at a first classifier neural network and at a watermark classifier neural network, wherein the first classifier neural network is trained using a first dataset and a watermark dataset. The first classifier neural network determines a classification label for the data sample. A watermark classifier neural network determines a watermark classification label for the data sample. A data sample is determined as an adversarial data sample based on the classification label for the data sample and the watermark classification label for the data sample.

Provided is an apparatus for embedding a plurality of forensic marks comprising: a pre-processing unit configured to: embed a watermark 0 symbol in each section content of an original content and store 0-section contents as a 0-content file and embed a watermark 1 symbol in each section content of the original content and store 1-section contents as a 1-content file; and embed random information in at least one section content among the 0-section contents and the 1-section contents and store random information section contents as a random information content file; and a distribution unit configured to: select corresponding section contents of the 0-content file and the 1-content file using predetermined information that is based on metadata; if a random information section content is present, select the random information section content instead of a 0-section content or a 1-section content; and output the selected random information section content as a distribution content.

A device for permitting access to a secure asset, the device including: at least one processor; and memory storing instructions to: receive a first image of a user from a credential device of the user, the first image being at least one of embedded with an encrypted key, encrypted, or comprising a watermark; at least one of verify the embedded encrypted key, decrypt the first image, or verify the watermark of the first image to obtain a verified image; generate a first representation of a face of the user using the verified image; capture a second image of the user and generate a second representation of the face of the user using the second image; determine a likelihood of a match between the first and second representations; and permit access by the user to the secure asset in instances where the likelihood of a match meets a threshold.

Systems and methods for determining the authenticity of vehicle operational data provided by telematics or other devices are provided. Vehicle performance and/or operational data may be collected and the authenticity of the data stream may be determined based on the whether the data stream includes a watermark in a predetermined location of the data stream or whether the data stream includes a data key comprising a predetermined false vehicle performance data reading. A second data recording device may also record vehicle performance and/or operational data. Both the first and second data recording devices may provide the respective vehicle performance data to a computing device. The computing device may compare the vehicle performance data from the first and second data recording devices to determine authenticity of the vehicle measurement data.

Systems and methods for payload encoding and decoding are disclosed. Example apparatus disclosed herein determine encoding data corresponding to one or more frequencies to be used to encode a message into audio data. Disclosed example apparatus also generate a license file including the encoding data, the license file corresponding to the message to be encoded into the audio data, the message to be encoded into the audio data by an encoding device. Disclosed example apparatus further transmit the license file to a decoding device to enable the decoding device to decode the message from the audio data based on the license file.

A system for applying fingerprinting/watermarking of consumer data, and analyzing “wild files” of consumer data to assign a guilt score for a particular party who may have leaked the data, allows the owner of data sources (“Data Owners”) to identify and assert ownership of textual data that has been distributed outside of their firewall in the clear (i.e., without encryption), either intentionally or unintentionally, and assign guilt to parties misusing the data. The system can be used by Data Owners who transmit, lease, or sell data to individuals or organizations (“Trusted Third Parties” or “TTPs”) to recognize and assert ownership of their data in the case where one or more TTPs leaks the data (the leaked file is defined as a “Leaked Subset”) into the hands of others (“Bad Actors”) who either knowingly or unknowingly use the data illegally.