StegaPos: Preventing Unwanted Crops and Replacements with Imperceptible Positional Embeddings

TL;DR

This paper introduces StegaPos, a novel steganography system that embeds imperceptible positional signatures in images to detect post-publication edits like cropping or splicing, enhancing image authenticity verification.

Contribution

The paper presents a learned, spatially-varying steganography method that encodes positional information to identify image alterations, a new approach for image integrity verification.

Findings

Reliable detection of image edits using learned positional signatures

Robustness of the system without perceptible image distortion

Effective for small images (400x400 pixels) with CNN architectures

Abstract

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.