Abstract
Image tampering/forgery can be accomplished easily using precise image editing software. However, different type of traces is introduced during image forgery process that may helpful in image forgery detection. This paper is an attempt to propose a robust blind image tampering detection technique in accordance with wavelet transform and texture descriptor. Image crucial details are highlighted using shift-invariant stationary wavelet transform. This transform provides more information about image internal statistics in comparison with DWT. Further to convert this information in terms of the feature vector, co-occurrence-based texture operator is used. The linear kernel SVM classifier is utilized to distinguish between pristine and forged images. The effectiveness of the proposed technique is proved on three image forgery evaluation databases, i.e., CASIA v1.0, Columbia and DSO-1.
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Agarwal, S., Chand, S. (2018). Image Forgery Detection Using Co-occurrence-Based Texture Operator in Frequency Domain. In: Sa, P., Sahoo, M., Murugappan, M., Wu, Y., Majhi, B. (eds) Progress in Intelligent Computing Techniques: Theory, Practice, and Applications. Advances in Intelligent Systems and Computing, vol 518. Springer, Singapore. https://doi.org/10.1007/978-981-10-3373-5_10
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DOI: https://doi.org/10.1007/978-981-10-3373-5_10
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