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We present a learned, spatially-varying steganography system that allowsdetecting when and how images have been altered by cropping, splicing orinpainting after publication. The system comprises a learned encoder thatimperceptibly hides distinct positional signatures in every local image regionbefore publication, and an accompanying learned decoder that extracts thesteganographic signatures to determine, for each local image region, its 2Dpositional coordinates within the originally-published image. Crop andreplacement edits become detectable by the inconsistencies they cause in thehidden positional signatures. Using a prototype system for small $(400 \times400)$ images, we show experimentally that simple CNN encoder and decoderarchitectures can be trained jointly to achieve detection that is reliable androbust, without introducing perceptible distortion. This approach could helpindividuals and image-sharing platforms certify that an image was published bya trusted source, and also know which parts of such an image, if any, have beensubstantially altered since publication.

StegaPos: Preventing Unwanted Crops and Replacements with Imperceptible Positional Embeddings