A New Hope for PRNU on Modern Smartphones? Evidence That Device-Specific Fingerprints Survive Computational Photography

Identifying the device that captured a digital image is a central task in image forensics, made more urgent by pervasive smartphone photography and the rise of AI-generated imagery. This paper compares three source camera identification (SCI) approaches - Photo Response Non-Uniformity (PRNU) analysis, JPEG compression artifact analysis, and a convolutional neural network (CNN) - on images from four smartphone devices (Xiaomi Redmi Note 10S, Samsung Galaxy S24, iPhone 13 Pro Max, and iPhone 17 Pro). For each method, discriminative features are extracted (DCT coefficient statistics for JPEG artifacts, denoised noise residuals for PRNU, and learned convolutional features for the CNN) and classified with an SVM (JPEG, PRNU) or trained end-to-end (CNN), using an identical fixed, stratified 70/30 train-test split for direct comparability. The DCT/JPEG-artifact-inspired classifier achieves the highest accuracy (0.90), followed by the PRNU-inspired method (0.71), while the CNN baseline reaches only 0.29. Under this experimental configuration, the DCT/statistical and PRNU-inspired approaches outperform the evaluated CNN baseline. The results motivate evaluation on larger, format-balanced datasets and investigation of hybrid or learned approaches under more robust validation protocols.

Passive fingerprints (intrinsic traces naturally left by generators) have been shown to enable attribution in speech deepfake detection, yet their persistence, reproducibility, and content-independence remain unverified. Moreover, no prior work distinguishes perceptible from imperceptible fingerprints, although the two have very different implications for attribution reliability. Perceptible fingerprints, such as emotional expression, are shaped by perceptual quality objectives and may change across model updates, whereas imperceptible fingerprints are not explicitly optimised by current training objectives and are rarely considered in existing dataset design or training strategies, as they have limited influence on downstream applications. We therefore propose a Perceptible-Imperceptible Passive-fingerprint Diagnostic Protocol (PIPDP) to define and separately analyze these two fingerprint types. PIPDP comprises three complementary analyses: multi-evidence fingerprint verification through residual-energy, reproducibility, and saliency analyses, perceptually transparent perturbations preserving audio quality, and prompt-driven emotion change that modifies perceptible fingerprints without model retraining. Experiments across ten speech generators and three attribution detectors show that imperceptible fingerprints provide persistent attribution cues. Perceptually transparent perturbations reduce attribution accuracy by up to 48.2% on HiggsAudioV3, whereas emotion-driven changes leave attribution largely unchanged, with only about a 1.0% accuracy variation across emotions on CosyVoice2 using w2v-bert-MLP. These results suggest that imperceptible fingerprints are more reliable for trustworthy attribution.

The objective of this work is to propose a novel methodology for the finger knuckle print recognition, which is essentially a digital photo of the finger-knuckle region. We have employed very simple concepts of visual computing such as a filter based on the Sobel operator for finding edges and a simple noise reduction algorithm. These operations are exceptionally fast and produce binary images, which are very efficient to process and to store. Furthermore, alongside this preprocessing, some similarity measures were also regarded and evaluated for the task. After preprocessing an input finger it is compared to all the images of fingers in the dataset, one by one. We have obtained up to 17.02% of successful recognitions (true positive rate) with a large dataset.