Reversible Visual Obfuscation via Embedded Recovery Metadata: Combining Median‑Filter Irreversibility with Cryptographic Restoration

Abstract

Although cloud storage platforms are widely used to safeguard personal images, data leakage and unauthorized access remain persistent threats, exposing sensitive visual content. Linear obfuscation methods such as Gaussian blur can be reversed by deconvolution when kernel parameters are known, while non-linear median filtering permanently destroys details but also eliminates any possibility of legitimate recovery. This study introduces a reversible obfuscation framework that combines the practical irreversibility of large-radius median filtering with an embedded, encrypted restoration payload. A sensitive image region is first blurred with a non-invertible median filter, making it resistant to both deconvolution and AI‑based reconstruction. Simultaneously, a losslessly compressed and AES‑256‑GCM‑encrypted copy of the original region is hidden in the least significant bits of the blurred block. Unauthorised viewers—including any party that intercepts the image through a cloud leak—see only the irreversibly obfuscated content; an authorised user with the correct passphrase can decrypt and restore the original region with pixel‑perfect fidelity. Experimental results on facial and document images demonstrate that the obfuscated region withstands state‑of‑the‑art AI restoration while the embedded payload enables lossless recovery in under 50 ms, with an average overhead of only 0.41 bits per pixel (using a single embedding channel) and no visible artefacts. The method offers a practical balance between strong, irreversible privacy and cryptographically controlled recoverability, making it suitable for secure archiving, forensic analysis, and privacy‑preserving communications.