IEEE Transactions on Information Forensics and Security · 2008 · 205 citations · 22 references
ComputingComputer ScienceComputer VisionDigital ForensicsDigital ImagingComputer ForensicsCounterfeitingDigital Image ProcessingForensic EvidenceImage ProcessingSignal Detection TheoryBicubic InterpolationForensic Image AnalysisForensic InvestigationImage Forensic AnalysisImage ForensicsImage Quality BenchmarkingResampling Detection
Resampling detection has become a standard tool for forensic analyses of digital images. The paper introduces new image transformation variants that evade resampling detectors based on periodic residual variations. The authors develop these undetectable transformations and benchmark their detectability and image quality against conventional linear, bicubic, and sinc interpolation. Experiments on a large image database show the proposed methods are effective, challenging the reliability of existing forensic tools and motivating the development of better techniques.
Resampling detection has become a standard tool for forensic analyses of digital images. This paper presents new variants of image transformation operations which are undetectable by resampling detectors based on periodic variations in the residual signal of local linear predictors in the spatial domain. The effectiveness of the proposed method is supported with evidence from experiments on a large image database for various parameter settings. We benchmark detectability as well as the resulting image quality against conventional linear and bicubic interpolation and interpolation with a sinc kernel. These early findings on ldquocounter-forensicrdquo techniques put into question the reliability of known forensic tools against smart counterfeiters in general, and might serve as benchmarks and motivation for the development of much improved forensic techniques.
22