Abstract:
Forensic analysis of images has important applications in security, court of law, insurance agencies, medical imaging, and electronic media. To develop robust image forgery detection and localization methods, applicable to real scenarios are highly needed. The focus of this thesis is to develop automatic image forgery detection and localization techniques. In the image forgery detection and localization, the main challenge is the robust representation of tampering traces.
First, to identify the research gaps for contribution, the state-of-the-art passive image forgery detection and localization techniques have been reviewed. The existing techniques are not robust in representing the tampering traces. To overcome this issue two techniques have been proposed.
The first technique has been developed to detect copy-move forgery in images by estimating noise patterns. To represent noise patterns, a new descriptor FFT-DRLBP employing Fast Fourier Transformation (FFT) and Discriminative Robust Local Binary Patterns (DRLBP) is introduced. Noise patterns are estimated using FFT, then the discrepancies in the noise patterns due to tampering are encoded using DRLBP. Support Vector Machine (SVM) is used to classify images as authentic or forged. This technique detects authentic and forged images with 99.21 % accuracy.
To localize the copy-move forgeries a robust FFT-SIFT descriptor based on FFT and Scale Invariant Feature Transform (SIFT) is proposed. Localization method based on FFT-SIFT descriptor outperforms state-of-the-art and achieves high true positive rate while maintaining low false positive rate.The second technique has been developed to detect splicing forgery in images by estimating noise inconsistencies. For this purpose, a new descriptor DWT-DRLBP is introduced based on Discrete Wavelet Transformation (DWT) and DRLBP. First image is decomposed using DWT, the texture variation in each DWT sub-band is encoded using DRLBP histograms. Cb and Cr components are used to extract features using DWT-DRLBP descriptor. For classification SVM is employed. The method offers excellent results (98.95 %) and outperforms the state-of-the-art methods.
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In addition, to validate the robustness of the proposed methods on real scenarios, a new dataset called Forged Real Images Throughout History (FRITH) is developed to validate the performance of the proposed methods. To further validate the robustness of the proposed methods, cross-dataset experiments are performed to analyze the applicability on unseen images.
