Artificial intelligence architectures for determining image authenticity

A non-transitory computer-readable medium including instructions for a trained machine-learning model that is executable by one or more processors to detect a manipulation of an image, the trained machine-learning model including:

• a constrained convolutional layer configured to employ a filter for detecting one or more image-manipulation fingerprints in the image and to generate a feature map associated with the image-manipulation fingerprints;
• a plurality of separable convolutional layers configured to perform a respective spatial convolution on each color channel of the image based on the feature map to generate a plurality of feature maps corresponding to the image;
• a global average-pooling layer configured to average the plurality of feature maps to generate a vector of feature values;
• and
• one or more additional layers configured to output, based on the vector of feature values, an indication of whether the image was manipulated or not manipulated.

A method comprising:

• receiving, by a processor, an image that is to be analyzed for authenticity;
• providing, by the processor, the image as input to a trained machine-learning model for detecting a manipulation of the image, wherein the trained machine-learning model includes:
• a constrained convolutional layer configured to employ a filter for detecting one or more image-manipulation fingerprints in the image and to generate a feature map associated with the image-manipulation fingerprints;
• a plurality of separable convolutional layers configured to perform a respective spatial convolution on each color channel of the image based on the feature map to generate a plurality of feature maps corresponding to the image;
• a global average-pooling layer configured to average the plurality of feature maps to generate a vector of feature values;
• and
• one or more additional layers configured to output, based on the vector of feature values, an indication of whether the image was manipulated or not manipulated;
• receiving, by the processor, the indication of whether the image was manipulated or not manipulated from the trained machine-learning model;
• and
• outputting, by the processor and based on the indication, a classification of the image as manipulated or not manipulated for display on a display device.