Abstract: A system and methods are disclosed for using a trained machine learning model to identify constituent images within composite images. A method may include providing data identifying a first image as input to a machine learning model trained using training data identifying a plurality of composite images that each include one or more constituent images, and determining, using one or more outputs of the trained machine learning model, that the first image is a composite image that includes a first constituent image, wherein at least a portion of the first constituent image is in a spatial area of the first image, and wherein the first constituent image corresponds to a frame of a video embedded into the first image.

Abstract: A system and methods are disclosed for using a trained machine learning model to identify constituent images within composite images. A method may include providing pixel data of a first image as input to the trained machine learning model, obtaining one or more outputs from the trained machine learning model, and extracting, from the one or more outputs, an indication that the first image is a composite image that includes a constituent image, wherein at least a portion of the constituent image is in a spatial area of the first image.

Abstract: A system and methods are disclosed for using a trained machine learning model to identify constituent images within composite images. A method may include providing pixel data of a first image as input to the trained machine learning model, obtaining one or more outputs from the trained machine learning model, and extracting, from the one or more outputs, an indication that the first image is a composite image that includes a constituent image, wherein at least a portion of the constituent image is in a spatial area of the first image.

Abstract: A system and methods are disclosed for using a trained machine learning model to identify constituent images within composite images. A method may include providing pixel data of a first image as input to the trained machine learning model, obtaining one or more outputs from the trained machine learning model, and extracting, from the one or more outputs, a level of confidence that (i) the first image is a composite image that includes a constituent image, and (ii) at least a portion of the constituent image is in a particular spatial area of the first image.

Abstract: A system and methods are disclosed for using a trained machine learning model to identify constituent images within composite images. A method may include providing pixel data of a first image as input to the trained machine learning model, obtaining one or more outputs from the trained machine learning model, and extracting, from the one or more outputs, a level of confidence that (i) the first image is a composite image that includes a constituent image, and (ii) at least a portion of the constituent image is in a particular spatial area of the first image.

Abstract: A system and methods are disclosed for training a machine learning model to identify constituent images within composite images. In one implementation, a composite image is generated, where the composite image comprises a first portion containing pixel data of a first constituent image, and a second portion containing pixel data of a second constituent image. A first training input comprising pixel data of the composite image and a first target output for the first training input are generated, where the first target output identifies a position of the first portion within the composite image. The training data is provided to train the machine learning model on (i) a set of training inputs comprising the first training input and (ii) a set of target outputs comprising the first target output.

Abstract: A system and methods are disclosed for training a machine learning model to identify constituent images within composite images. In one implementation, a composite image is generated, where the composite image comprises a first portion containing pixel data of a first constituent image, and a second portion containing pixel data of a second constituent image. A first training input comprising pixel data of the composite image and a first target output for the first training input are generated, where the first target output identifies a position of the first portion within the composite image. The training data is provided to train the machine learning model on (i) a set of training inputs comprising the first training input and (ii) a set of target outputs comprising the first target output.