Abstract: A method may include embedding, in a hidden layer and/or an output layer of a first machine learning model, a first digital watermark. The first digital watermark may correspond to input samples altering the low probabilistic regions of an activation map associated with the hidden layer of the first machine learning model. Alternatively, the first digital watermark may correspond to input samples rarely encountered by the first machine learning model. The first digital watermark may be embedded in the first machine learning model by at least training, based on training data including the input samples, the first machine learning model. A second machine learning model may be determined to be a duplicate of the first machine learning model based on a comparison of the first digital watermark embedded in the first machine learning model and a second digital watermark extracted from the second machine learning model.

Abstract: Various methods are provided for generating motion vectors in the context of 3D computer-generated images. An example method includes generating, for each pixel of one or more objects to be rendered in a current frame, a 1-phase motion vector (MV1) and a 0-phase motion vector (MV0), each MV1 and MV0 having an associated depth value, to thereby form an MV1 texture and an MV0 texture; converting the MV1 texture to a set of MV1 blocks and converting the MV0 texture to a set of MV0 blocks; and outputting the set of MV1 blocks and the set of MV0 blocks for image processing.

Abstract: Various methods are provided for the generation of motion vectors in the context of 3D computer-generated images. In one example, a method includes generating, for each pixel of one or more objects to be rendered in a current frame, a 1-phase motion vector (MV1) and a 0-phase motion vector (MV0), each MV1 and MV0 having an associated depth value, to thereby form an MV1 texture and an MV0 texture, each MV0 determined based on a camera MV0 and an object MV0, and outputting MV1 texture and the MV0 texture for image processing.

Abstract: A method may include embedding, in a hidden layer and/or an output layer of a first machine learning model, a first digital watermark. The first digital watermark may correspond to input samples altering the low probabilistic regions of an activation map associated with the hidden layer of the first machine learning model. Alternatively, the first digital watermark may correspond to input samples rarely encountered by the first machine learning model. The first digital watermark may be embedded in the first machine learning model by at least training, based on training data including the input samples, the first machine learning model. A second machine learning model may be determined to be a duplicate of the first machine learning model based on a comparison of the first digital watermark embedded in the first machine learning model and a second digital watermark extracted from the second machine learning model.