Shaunagh Downing | Searcharxiv

arXiv2025

Using Wavelet Domain Fingerprints to Improve Source Camera Identification

Camera fingerprint detection plays a crucial role in source identification and image forensics, with wavelet denoising approaches proving particularly effective for extracting sensor pattern noise (SPN). In this article, we introduce the concept of a wavelet domain (WD) fingerprint, redefining the representation of the extracted fingerprint from the conventional image domain to the native wavelet coefficient domain. Rather than reconstructing the fingerprint as a spatial domain image, fingerprint comparison is performed directly on the wavelet coefficients, eliminating the final inverse transform and subsequent image-domain post-processing. This reformulation streamlines the fingerprint extraction and comparison pipeline while preserving the information required for source camera identification. The proposed framework is applicable to existing wavelet-based SPN extraction methods and is demonstrated using two representative state-of-the-art pipelines. Experimental results on real-world datasets show that the proposed approach significantly reduces computational cost, making it well-suited for large-scale source camera identification applications.

cs.CV↗

arXiv2023

Optimising seismic imaging design parameters via bilevel learning

Full Waveform Inversion (FWI) is a standard algorithm in seismic imaging. Its implementation requires the a priori choice of a number of "design parameters", such as the positions of sensors for the actual measurements and one (or more) regularisation weights. In this paper we describe a novel algorithm for determining these design parameters automatically from a set of training images, using a (supervised) bilevel learning approach. In our algorithm, the upper level objective function measures the quality of the reconstructions of the training images, where the reconstructions are obtained by solving the lower level optimisation problem -- in this case FWI. Our algorithm employs (variants of) the BFGS quasi-Newton method to perform the optimisation at each level, and thus requires the repeated solution of the forward problem -- here taken to be the Helmholtz equation. This paper focuses on the implementation of the algorithm. The novel contributions are: (i) an adjoint-state method for the efficient computation of the upper-level gradient; (ii) a complexity analysis for the bilevel algorithm, which counts the number of Helmholtz solves needed and shows this number is independent of the number of design parameters optimised; (iii) an effective preconditioning strategy for iteratively solving the linear systems required at each step of the bilevel algorithm; (iv) a smoothed extraction process for point values of the discretised wavefield, necessary for ensuring a smooth upper level objective function. The algorithm also uses an extension to the bilevel setting of classical frequency-continuation strategies, helping avoid convergence to spurious stationary points. The advantage of our algorithm is demonstrated on a problem derived from the standard Marmousi test problem.

math.NA↗