StegaPos: Preventing Unwanted Crops and Replacements with Imperceptible Positional Embeddings
This addresses the need for individuals and platforms to verify image authenticity and detect edits, though it is incremental as it builds on existing steganography and detection methods.
The paper tackles the problem of detecting image alterations like cropping and splicing by developing a steganography system that embeds imperceptible positional signatures, achieving reliable detection without perceptible distortion in 400x400 images.
We present a learned, spatially-varying steganography system that allows detecting when and how images have been altered by cropping, splicing or inpainting after publication. The system comprises a learned encoder that imperceptibly hides distinct positional signatures in every local image region before publication, and an accompanying learned decoder that extracts the steganographic signatures to determine, for each local image region, its 2D positional coordinates within the originally-published image. Crop and replacement edits become detectable by the inconsistencies they cause in the hidden positional signatures. Using a prototype system for small $(400 \times 400)$ images, we show experimentally that simple CNN encoder and decoder architectures can be trained jointly to achieve detection that is reliable and robust, without introducing perceptible distortion. This approach could help individuals and image-sharing platforms certify that an image was published by a trusted source, and also know which parts of such an image, if any, have been substantially altered since publication.