Embodiments of systems and methods for methods for the intelligent orchestration of digital watermarking using a platform framework are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: receive a notification, via a platform framework, of a communication session; and in response to the notification, apply a digital watermark, via the platform framework, to at least a portion of content shared during the communication session.

A computer system for the creation of subliminal image or watermarks where the apparent video is an “obvious” image and a subliminal or “clandestine” image or watermark is hidden within the data structure. The concept is envisioned for the cloaking of images, sound, video or like digitized non-coded information. The cloaked files may be intended for storage, transmission, or clandestine placement in a public file system. The watermark may be used for the subliminal marking of a source of a file, its creation, or for tagging copyrighted information.

A method and data processing system are provided for determining if a machine learning model has been copied. The machine learning model has a plurality of nodes, the plurality of nodes is organized as a plurality of interconnected layers, and the plurality of interconnected layers includes an input layer and an output layer. The output layer has a predetermined number of output nodes for classifying input samples into a predetermined number of categories, where each output node corresponds to a category. An additional watermarking node is added to the output layer. The model is trained to classify the input data into the predetermined number of categories and into an additional category for the additional node. The additional node may be added to another model to determine if the another model is a copy or clone of the ML model.

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.

Embodiments of systems and methods for methods for the intelligent orchestration of video or image mirroring using a platform framework are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: receive a notification, via a platform framework, of a communication session; and in response to the notification, apply a video or image mirroring operation, via the platform framework, to at least a portion of content shared during the communication session.

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.

Embodiments of the disclosure relates to signing of an artificial intelligence (AI) model with a watermark for a data processing (DP) accelerator. In one embodiment, in response to a request received by the data processing accelerator, the request sent by an application to embed digital rights protection to an AI model, a system generates a watermark for the AI model based on a watermark algorithm. The system embeds the watermark onto the AI model. The system signs the AI model having the embedded watermark to generate a signature. The system returns the signature and the AI model having the embedded watermark back to the application, where the signature is used to authenticate the watermark and/or the AI model.

Provided is a system for privately sharing a verified video which ensures that a video recorded in real time by a video creator is not tampered with by a third party and thus prevents the original video from being edited and maliciously used.

Provided is a computer-implemented video processing method. The method comprises receiving a stream of original images related to a video access event and creating a stream of output images corresponding to the original images. The output images include first images comprising a hidden digital forensic marker and second images comprising a visible digital forensic marker. The hidden marker and the visible marker each encode information related to the video access event. The stream of output images is output onto a network or caused to be displayed on a screen. The visible marker serves as a deterrent for distribution or recording, and if an attempt is made to remove it, the hidden marker remains in the image, allowing the information relevant to the video access event to be recovered. Also provided are a method of integrity detection for a stream of images containing markers, and a method of embedding a dynamic marker.

Systems and methods for encoding multiple video streams with digital watermarking for adaptive bitrate streaming in accordance with embodiments of the invention are disclosed. In one embodiment, a method for preprocessing multimedia content into streams with watermark information includes receiving a source content media stream, generating at least two variant preprocessed streams for each received source content media stream, where each variant preprocessed stream includes different watermark information in the same locations as the other variant preprocessed streams and where marked locations are spaced apart at least a distance equal to a predetermined maximum segment size, generating a set of embed location information describing marked locations in the variant preprocessed streams, generating at least one variant output stream from each variant preprocessed stream using video compression, partitioning each variant output stream into a set of segments, where each segment is no longer than the predetermined maximum segment size and contains at most one copy of the watermark information, generating a set of segment boundary information describing the boundaries of segments within the variant output streams and the boundaries are the same between variant output streams, and generating a segment selection list using the set of embed location information and the set of segment boundary information, where the segment selection list includes only one variant segment for each segment according to a watermark sequence and the digits of the watermark sequence correspond to the watermark information applied to each variant preprocessed stream.