Source Camera Identification using Non-decimated Wavelet Transform

Source Camera identification of digital images can be performed by matching the sensor pattern noise (SPN) of the images with that of the camera reference signature. This paper presents a non-decimated wavelet based source camera identification method for digital images. The proposed algorithm applies a non-decimated wavelet transform on the input image and split the image into its wavelet sub-bands. The coefficients within the resulting wavelet high frequency sub-bands are filtered to extract the SPN of the image. Cross correlation of the image SPN and the camera reference SPN signature is then used to identify the most likely source device of the image. Experimental results were generated using images of ten cameras to identify the source camera of the images. Results show that the proposed technique generates superior results to that of the state of the art wavelet based source camera identification

Pigment Melanin: Pattern for Iris Recognition

Recognition of iris based on Visible Light (VL) imaging is a difficult problem because of the light reflection from the cornea. Nonetheless, pigment melanin provides a rich feature source in VL, unavailable in Near-Infrared (NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical not stimulated in NIR. In this case, a plausible solution to observe such patterns may be provided by an adaptive procedure using a variational technique on the image histogram. To describe the patterns, a shape analysis method is used to derive feature-code for each subject. An important question is how much the melanin patterns, extracted from VL, are independent of iris texture in NIR. With this question in mind, the present investigation proposes fusion of features extracted from NIR and VL to boost the recognition performance. We have collected our own database (UTIRIS) consisting of both NIR and VL images of 158 eyes of 79 individuals. This investigation demonstrates that the proposed algorithm is highly sensitive to the patterns of cromophores and improves the iris recognition rate.Comment: To be Published on Special Issue on Biometrics, IEEE Transaction on Instruments and Measurements, Volume 59, Issue number 4, April 201

Image source identification and characterisation for forensic analysis

Digital imaging devices, such as digital cameras or mobile phones, are prevalent in society. The images created by these devices can be used in the commission of crime. Source device identification is an emerging research area and involves the identification of artefacts that are left behind in an image by the camera pipeline. These artefacts can be used as digital signatures to identify the source device forensically. The type of digital signature considered in this thesis is the Sensor Pattern Noise (SPN), which consists mainly of the PRNU (Photo Response Non-Uniformity) of the imaging device. The PRNU is unique to each individual sensor, which can be extracted traditionally with a wavelet denoising filter and enhanced to attenuate unwanted artefacts. This thesis proposes a novel method to extract the PRNU of a digital image by using Singular Value Decomposition (SVD) to extract the digital signature. The extraction of the PRNU is performed using the homomorphic filtering technique, where the inherently nonlinear PRNU is transformed into an additive noise. The range of the energy of the PRNU is estimated, which makes it easier to separate from other polluting components to obtain a cleaner signature, as compared to extracting all the high frequency signals from an image. The image is decomposed by using SVD, which separates the image into ranks of descending order of energies. The estimated energy range of the PRNU is used to obtain the interesting ranks that are utilised to form part of the digital signature. A case study of an existing image analyser platform was performed by investigating its identification and classification results. The SVD based extraction method was tested by extracting image signatures from camera phones. The results of the experiments show that it is possible to determine the source device of digital images

Are Social Networks Watermarking Us or Are We (Unawarely) Watermarking Ourself?

In the last decade, Social Networks (SNs) have deeply changed many aspects of society, and one of the most widespread behaviours is the sharing of pictures. However, malicious users often exploit shared pictures to create fake profiles leading to the growth of cybercrime. Thus, keeping in mind this scenario, authorship attribution and verification through image watermarking techniques are becoming more and more important. In this paper, firstly, we investigate how 13 most popular SNs treat the uploaded pictures, in order to identify a possible implementation of image watermarking techniques by respective SNs. Secondly, on these 13 SNs, we test the robustness of several image watermarking algorithms. Finally, we verify whether a method based on the Photo-Response Non-Uniformity (PRNU) technique can be successfully used as a watermarking approach for authorship attribution and verification of pictures on SNs. The proposed method is robust enough in spite of the fact that the pictures get downgraded during the uploading process by SNs. The results of our analysis on a real dataset of 8,400 pictures show that the proposed method is more effective than other watermarking techniques and can help to address serious questions about privacy and security on SNs.Comment: 43 pages, 6 figure

Are Social Networks Watermarking Us or Are We (Unawarely) Watermarking Ourself?

In the last decade, Social Networks (SNs) have deeply changed many aspects of society, and one of the most widespread behaviours is the sharing of pictures. However, malicious users often exploit shared pictures to create fake profiles, leading to the growth of cybercrime. Thus, keeping in mind this scenario, authorship attribution and verification through image watermarking techniques are becoming more and more important. In this paper, we firstly investigate how thirteen of the most popular SNs treat uploaded pictures in order to identify a possible implementation of image watermarking techniques by respective SNs. Second, we test the robustness of several image watermarking algorithms on these thirteen SNs. Finally, we verify whether a method based on the Photo-Response Non-Uniformity (PRNU) technique, which is usually used in digital forensic or image forgery detection activities, can be successfully used as a watermarking approach for authorship attribution and verification of pictures on SNs. The proposed method is sufficiently robust, in spite of the fact that pictures are often downgraded during the process of uploading to the SNs. Moreover, in comparison to conventional watermarking methods the proposed method can successfully pass through different SNs, solving related problems such as profile linking and fake profile detection. The results of our analysis on a real dataset of 8400 pictures show that the proposed method is more effective than other watermarking techniques and can help to address serious questions about privacy and security on SNs. Moreover, the proposed method paves the way for the definition of multi-factor online authentication mechanisms based on robust digital features