System and methods for authenticating content
The invention relates generally to the field of content authentication, and more particularly, to a system and methods for verifying the authenticity of content output to a user. In certain preferred embodiments, the content is verified by identifying the source data of the content, distributing the content, and authenticating the distributed content. Where the content has not been changed, the system may authenticate the content using a cryptographic hash. When minor changes to the content are made, the system may use a perceptual hash to authenticate the content. Further, the system may utilize machine learning algorithms to identify patterns between the same content in, for example, multiple formats and sizes. Advantageously, the content that is uploaded to the system may be used to train machine-learning models that the system may use to authenticate content that has been converted but unmanipulated.
1. A system for authenticating content comprising:
a processor;
a non-volatile, non-transitory memory in communication with the processor via a communication infrastructure, said memory including stored instructions that, when executed by said processor, cause said processor to:
verifying that content is from a trusted source, wherein said trusted source is verified via an audit;
analyze the content to define one or more attributes corresponding to the content, wherein said one or more attributes is metadata including at least one variable associated with the content;
generate a modal window, said modal window including the content and the one or more attributes;
distribute the content through use of the modal window; and
authenticate, in response to a request via the modal window, the distributed contents, wherein said modal window is an inline frame including an interactive element corresponding to an authentication of the distributed content.
2. The analyzing step of claim 1 , wherein the attribute is defined using a cryptographic hash function of a fixed length.
3. The analyzing step of claim 1 , wherein the attribute is defined using a perceptual hash function.
4. The analyzing step of claim 1 , wherein the attribute is defined based on audio fingerprinting.
5. The analyzing step of claim 1 , wherein the attribute is defined by applying a checksum algorithm to the content.
6. The authenticating step of claim 1 , wherein the processor is further operative to determine whether the one or more attributes of the content match to one or more attributes of the distributed content.
7. The system of claim 1 , wherein said interactive element is at least one of a watermark and a seal.
8. The system of claim 1 , wherein the interactive element corresponds to a trustworthiness associated with the trusted source.
9. The system of claim 1 , wherein said processor is further configured to:
locate a data set containing content, wherein the set includes original content and variations of the original content; and
process the data set using a machine learning algorithm to produce a machine learning model to identify minor changes to content characteristics.
10. A method for authenticating content comprising:
verifying content is from a trusted source, wherein said trusted source is verified via an audit;
analyzing content to define one or more attributes corresponding to the content, wherein said one or more attributes is metadata including at least one variable associated with the content;
generating a modal window, said modal window including the content and the one or more attributes;
distributing the content through use of the modal window; and
authenticating, in response to a request via the modal window, the distributed content, wherein said modal window is an inline frame further including an interactive element corresponding to an authentication of the distributed content.
11. The analyzing step of claim 10 , wherein the attribute is defined using at least one of a cryptographic hash, perceptual hash, and audio fingerprinting.
12. The analyzing step of claim 10 , wherein the attribute is metadata, wherein the metadata corresponds to at least one of pixel count, resolution, file size, frame count, audio frequency mapping, and file type.
13. The authenticating step of claim 10 , further comprising determining whether the one or more attributes of the content match to one or more attributes of the distributed content.
14. The method of claim 10 , wherein the modal window further includes at least one of a watermark and a seal corresponding to a trustworthiness of the trusted source.
15. The method of claim 10 , further comprising:
locating a data set containing content, wherein the set includes original content and variations of the original content; and
processing the data set using a machine learning algorithm to produce a machine learning model to identify minor changes to content characteristics.