In this paper we proposed a novel technique for watermarking facial images into different background images in an invisible manner. This facial image can be extracted blindly from the watermarked image without any prior knowledge about the background image (blindly). More over the proposed technique can still deliver an extracted face image even in the presence of noise, compression or cropping of the watermarked image, but with some visual quality degradation. This embedding/extraction methodology is fake-free and robust against compression, noise and cropping attacks. The proposed approach consists of an insertion part and an extraction part. The proposed approach is based upon the natural preserve transform technique that achieves a balance between energy concentration and energy distribution; hence it is best used for watermarking images. Extensive simulation results on several facial and background images, that demonstrate the effectiveness of the proposed technique, are illustrated in comparison with current watermarking methods in the literature.

The detection of JPEG prior compression has become an essential task in the detection of forgery in image forensics. In this paper we propose a novel DCT implementation technique that can be utilized in the detection of any hacking or tampering of JPEG/DCT compressed images. The proposed approach is based upon recent literature ideas of recompressing JPEG image blocks and detecting if this block has been compressed before or not and how many times. In this paper we proposed a DCT implementation that has a onetime signature on processed coefficients or pixels and can be used as a tool to detect if this block has been compressed before using the proposed implementation or not. Any further processing can be easily detected and identified. The proposed DCT transformation is nonorthogonal and results in a minor amount of error due to this nonorthogonality, however it maintains an excellent tradeoff between compression performance, and transform error. Illustrative examples on several processed images are presented with complexity analysis.

In this paper we propose an interactive super resolution coding technique to enhance user’s visualization of received low resolution (LR) images. The proposed technique hides/embeds high frequencies and edges in the LR image, and constructs a high resolution (HR) image upon a user click. The proposed technique is suitable for many applications (eg. digital libraries) that display images and videos to the user in small sizes, and should be capable of reconstructing a HR version of the image or video upon a click on the LR version. The proposed approach consists of an insertion part and an extraction part. In the insertion part (that could be treated as an acquisition algorithm), high frequency components of the sensed image are embedded/inserted among the content of the finally delivered LR image in an invisible manner. The resulting sensed image is a regular low resolution image, however when performing the extraction part on it, a HR image is constructed without any side information in a blind, fast, robust and effective manner. The proposed approach has been tested on several images and video sequences against noise and compression, and gave very competitive results with current literature methods.

Exponential B-spline functions are more flexible than cardinal B-spline polynomials due to the extra degrees of freedom inherited by the arbitrary choice of its parameters. In this paper, independent simple proofs of some of the important features of Exponential B-spline functions are given. A novel efficient technique has also been proposed for decomposing a signal in terms of its exponential B-spline expansion. Applications of Exponential B-spline functions in spatial image compression are demonstrated. Our illustrative results show that Exponential B-splines outperform cardinal B-splines in image compression.

Many forensic techniques recently tried to detect the tampering and manipulation of JPEG compressed images that became a critical problem in image authentication and origin tracking. Some techniques indicated that a knowledgeable attacker can make it very hard to trace the image origin, while others indicated that portions of the compressed image that has been compressed at different quality factor quantization matrices are distinguishable if they are recompressed at a higher quality factor quantization matrix (with less quantization steps). In this paper, we pursue the idea of recompressing forensically suspect-able images with different compression parameters. We use different quantization matrix sizes that would indicate a DCT projection at different frequencies (horizontally, vertically, and diagonally), and would make it easier to track any tampering or hacking footprints. We show that a JPEG compressed image can make these footprints distinguishable if recompressed with a smaller size quantization matrix. Illustrative examples are presented.

B-splines caught interest of many engineering applications due to their merits of being flexible and provide a large degree of differentiability and cost/quality trade off relationship. However they have less impact with continuous time applications as they are constructed from piecewise polynomials. On the other hand, Exponential spline polynomials (E-splines) represent the best smooth transition between continuous and discrete domains as they are made of exponential segments. In this paper we present a technique for utilizing E-splines in image de-noising applications. This technique is based upon sub-band decomposition of the image through an E-spline based perfect reconstruction (PR) system. Different thresholdings are applied on the decomposition layers for de-noising purposes. Due to the selective nature of E-spline based decomposition, the performance of our E-spline based de-noising technique outperforms all other literature techniques.

B-splines caught interest of many engineering applications due to their merits of being flexible and provide a large degree of differentiability and cost/quality trade off relationship. However they have less impact with continuous time applications as they are constructed from piecewise polynomials. On the other hand, Exponential spline polynomials (E-splines) represent the best smooth transition between continuous and discrete domains as they are made of exponential segments. In this paper we present a technique for utilizing E-splines in image de-noising applications. This technique is based upon sub-band decomposition of the image through an E-spline based perfect reconstruction (PR) system. Different thresholdings are applied on the decomposition layers for de-noising purposes. Due to the selective nature of E-spline based decomposition, the performance of our E-spline based de-noising technique outperforms all other literature techniques.

B-splines caught interest of many engineering applications due to their merits of being flexible and provide a large degree of differentiability and cost/quality trade off relationship. However they have less impact with continuous time applications as they are constructed from piecewise polynomials. On the other hand, Exponential spline polynomials (E-splines) represent the best smooth transition between continuous and discrete domains as they are made of exponential segments. In this paper we present a technique for utilizing E-splines in image compression and de-noising applications. This technique is based upon sub-band decomposition of the image through an E-spline based perfect reconstruction (PR) system. Different thresholdings are applied on the decomposition layers for de-noising purposes. Due to the selective nature of E-spline based decomposition, the performance of our E-spline based de-noising technique outperforms all other literature techniques.

B-splines caught interest of many engineering applications due to their merits of being flexible and provide a large degree of differentiability and cost/quality trade off relationship. However they have less impact with continuous time applications as they are constructed from piecewise polynomials. On the other hand, Exponential spline polynomials (E-splines) represent the best smooth transition between continuous and discrete domains as they are made of exponential segments. In this paper we present a technique for utilizing E-splines in image compression and de-noising applications. This technique is based upon sub-band decomposition of the image through an E-spline based perfect reconstruction (PR) system. Different thresholdings are applied on the decomposition layers for de-noising purposes. Due to the selective nature of E-spline based decomposition, the performance of our E-spline based de-noising technique outperforms all other literature techniques.

Exponential spline polynomials (E-splines) represent the best smooth transition between continuous and discrete domains. As they are constructed from convolution of exponential segments, there are many degrees of freedom to optimally choose the most convenient E-spline, suitable for a specific application. In this paper, the parameters of these Esplines were optimally chosen, to enhance the performance of image de-noising as well as image zooming schemes. The proposed technique is based on minimizing the total variation function of the detail coefficients of the E-spline based wavelet decomposition. In image de-noising schemes, apart from Espline parameter estimations, the thresholding levels of their detail coefficients, are also optimally chosen. In zooming applications, the quality of interpolated images are further improved and sharpened by applying ICA technique to them, in order to remove any dependency. Illustrative examples are given to verify image enhancement of the proposed e-spline scheme, when compared with the existing approaches.