Smartphone Camera De-identification while Preserving Biometric Utility
This work addresses personal privacy and sensor forensics by enabling de-identification of smartphone camera images without compromising biometric utility, though it is incremental as it builds on existing PRNU and DCT methods.
The authors tackled the problem of smartphone camera de-identification by developing an algorithm that suppresses sensor-specific details and incorporates spoofing patterns while preserving biometric matching utility, achieving efficacy on three PRNU-based identification schemes across two datasets with 12 smartphone cameras.
The principle of Photo Response Non Uniformity (PRNU) is often exploited to deduce the identity of the smartphone device whose camera or sensor was used to acquire a certain image. In this work, we design an algorithm that perturbs a face image acquired using a smartphone camera such that (a) sensor-specific details pertaining to the smartphone camera are suppressed (sensor anonymization); (b) the sensor pattern of a different device is incorporated (sensor spoofing); and (c) biometric matching using the perturbed image is not affected (biometric utility). We employ a simple approach utilizing Discrete Cosine Transform to achieve the aforementioned objectives. Experiments conducted on the MICHE-I and OULU-NPU datasets, which contain periocular and facial data acquired using 12 smartphone cameras, demonstrate the efficacy of the proposed de-identification algorithm on three different PRNU-based sensor identification schemes. This work has application in sensor forensics and personal privacy.