  We present a model for differentiating between images that are authentic
copies of ones published by photographers, and images that have been
manipulated by cropping, splicing or downsampling after publication. The model
comprises an encoder that resides with the photographer and a matching decoder
that is available to observers. The encoder learns to embed imperceptible
positional signatures into image values prior to publication. The decoder
learns to use these steganographic positional ("stegapos") signatures to
determine, for each small image patch, the 2D positional coordinates that were
held by the patch in its originally-published image. Crop, splice and
downsample edits become detectable by the inconsistencies they cause in the
hidden positional signatures. We find that training the encoder and decoder
together produces a model that imperceptibly encodes position, and that enables
superior performance on established benchmarks for splice detection and high
accuracy on a new benchmark for crop detection.
