An image forensics system estimates a camera response function (CRF) associated with a digital image, and compares the estimated CRF to a set of rules and compares the estimated CRF to a known CRF. The known CRF is associated with a make and a model of an image sensing device. The system applies a fusion analysis to results obtained from comparing the estimated CRF to a set of rules and from comparing the estimated CRF to the known CRF, and assesses the integrity of the digital image as a function of the fusion analysis.

Using various embodiments, methods and systems for verification of a digital asset owner in a digital environment are described. In one embodiment, a system is configured to receive a non-fungible token (NFT) associated with a digital asset, the NFT providing proof of ownership of the digital asset through a cryptographic public key and retrieve the digital asset. The system then retrieves a secret pattern from the digital asset, wherein the secret pattern was previously embedded into the digital asset, the secret pattern associated with the cryptographic public key and computes a first identification hash value using a hash function, the hash function receiving a parameter value derived from the secret pattern. The system then receives a second identification hash value and compares the first identification hash value to the second identification hash value. If the first and second identification hash values are identical, then the digital asset is determined to be authentic.

The herein disclosed technology provides methods and systems that utilize machine learning solutions to identify web-based channels that are sources pirated copyright material, such as by using a machine learning classifier that is trained on historical copyright piracy data and channel features that may be determined and analyzed for each of a collection of channels without analyzing video or audio content of the channel.