Jonathan Goh

Bio: Jonathan Goh is an academic researcher from Agency for Science, Technology and Research. The author has contributed to research in topics: Digital watermarking & Watermark. The author has an hindex of 9, co-authored 9 publications receiving 370 citations. Previous affiliations of Jonathan Goh include Institute for Infocomm Research Singapore.

Image Region Forgery Detection: A Deep Learning Approach

Abstract: In digital forensics, the detection of the presence of tampered images is of significant importance. The problem with the existing literature is that majority of them identify certain features in images tampered by a specific tampering method (such as copy-move, splicing, etc). This means that the method does not work reliably across various tampering methods. In addition, in terms of tampered region localization, most of the work targets only JPEG images due to the exploitation of double compression artifacts left during the re-compression of the manipulated image. However, in reality, digital forensics tools should not be specific to any image format and should also be able to localize the region of the image that was modified. In this paper, we propose a two stage deep learning approach to learn features in order to detect tampered images in different image formats. For the first stage, we utilize a Stacked Autoencoder model to learn the complex feature for each individual patch. For the second stage, we integrate the contextual information of each patch so that the detection can be conducted more accurately. In our experiments, we were able to obtain an overall tampered region localization accuracy of 91.09% over both JPEG and TIFF images from CASIA dataset, with a fall-out of 4.31% and a precision of 57.67% respectively. The accuracy over the JPEG tampered images is 87.51%, which outperforms the 40.84% and 79.72% obtained from two state of the art tampering detection approaches.

Fingerprint Liveness Detection From Single Image Using Low-Level Features and Shape Analysis

Abstract: Fingerprint-based authentication systems have developed rapidly in the recent years. However, current fingerprint-based biometric systems are vulnerable to spoofing attacks. Moreover, single feature-based static approach does not perform equally over different fingerprint sensors and spoofing materials. In this paper, we propose a static software approach. We propose to combine low-level gradient features from speeded-up robust features, pyramid extension of the histograms of oriented gradient and texture features from Gabor wavelet using dynamic score level integration. We extract these features from a single fingerprint image to overcome the issues faced in dynamic software approaches, which require user cooperation and longer computational time. A experimental analysis done on LivDet 2011 data produced an average equal error rate (EER) of 3.95% over four databases. The result outperforms the existing best average EER of 9.625%. We also performed experiments with LivDet 2013 database and achieved an average classification error rate of 2.27% in comparison with 12.87% obtained by the LivDet 2013 competition winner.

Time-spread echo-based audio watermarking with optimized imperceptibility and robustness

Abstract: We present a time-spread echo-based audio watermarking scheme with optimized imperceptibility and robustness. Specifically, convex optimization based finite-impulse-response (FIR) filter design is utilized to obtain the optimal echo filter coefficients. The desired power spectrum of the echo filter is shaped by the proposed maximum power spectral margin (MPSM) and the absolute threshold of hearing (ATH) of human auditory system (HAS) to ensure the optimal imperceptibility. Meanwhile, the auto-correlation function of the echo filter coefficients is specified as the constraint in the problem formulation, which controls the robustness in terms of watermark detection. In this way, a joint optimization of imperceptibility and robustness can be quantitatively performed. As a result, the proposed watermarking scheme is superior to existing solutions such as the ones based on pseudo noise (PN) sequence or modified pseudo noise (MPN) sequence. Note that the designed echo kernel is also highly secure in that only with the same filter coefficients can one successfully detect the watermark. Experimental results are provided to evaluate the imperceptibility and robustness of the proposed watermarking scheme.

Audio Authentication by Exploring the Absolute-Error-Map of ENF Signals

Abstract: Recently, the electric network frequency (ENF), a natural signature embedded in many audio recordings, has been utilized as a criterion to examine the authenticity of audio recordings. ENF-based audio authentication system involves extraction of the ENF signal from a questioned audio recording, and matching it with the reference signal stored in an ENF database. This establishes a popular application of audio timestamp verification. In this paper, we explore another important application, i.e., ENF-based audio tampering detection, which has received less research attention. Specifically, we introduce the absolute-error-map (AEM) between the ENF signals obtained from the testing audio recording and the database. The AEM serves as an ensemble of the raw data associated with the ENF matching process. Through intensive analysis of the AEM, we propose two algorithms to jointly deal with timestamp verification and tampering detection, including insertion, deletion, and splicing attacks, respectively. The first algorithm is based on exhaustive point search and measurement, while the second algorithm leverages the image erosion technique to achieve fast detection of tampering type and tampered region, thus the second algorithm sacrifices some accuracy for speed. The authentication mechanism is that the system first determines if the testing data have been tampered with, and then outputs the timestamp information if no tampering is detected. Otherwise, it outputs the tampering type and tampered region. We demonstrate the effectiveness of the proposed solution via both synthetic and practical examples from our practically deployed audio authentication system.

A deep learning approach to detection of splicing and copy-move forgeries in images

Abstract: In this paper, we present a new image forgery detection method based on deep learning technique, which utilizes a convolutional neural network (CNN) to automatically learn hierarchical representations from the input RGB color images. The proposed CNN is specifically designed for image splicing and copy-move detection applications. Rather than a random strategy, the weights at the first layer of our network are initialized with the basic high-pass filter set used in calculation of residual maps in spatial rich model (SRM), which serves as a regularizer to efficiently suppress the effect of image contents and capture the subtle artifacts introduced by the tampering operations. The pre-trained CNN is used as patch descriptor to extract dense features from the test images, and a feature fusion technique is then explored to obtain the final discriminative features for SVM classification. The experimental results on several public datasets show that the proposed CNN based model outperforms some state-of-the-art methods.

Digital Signal Processing A Computer Based Approach

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Overview of the 2nd Author Profiling Task at PAN 2014

Abstract: The PAN task on author profiling has been organised in the framework of the WIQ-EI IRSES project (Grant No. 269180) within the FP 7 Marie Curie People Framework of the European Commission. We would like to thank Atribus by Corex for sponsoring the award for the winner team. We thank Julio Gonzalo, Jorge Carrillo and Damiano Spina from UNED for helping with the Twitter subcorpus. The work of the first author was partially funded by Autoritas Consulting SA and by Ministerio de Economia y Competitividad de Espana under grant ECOPORTUNITY IPT-2012-1220-430000 and CSO2013-43054-R. The work of the second author was in the framework the DIANA-APPLICATIONS-Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) project, and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.

On Optimal Power Allocation for Downlink Non-Orthogonal Multiple Access Systems

Abstract: Non-orthogonal multiple access (NOMA) enables power-domain multiplexing via successive interference cancellation (SIC) and has been viewed as a promising technology for 5G communication. The full benefit of NOMA depends on resource allocation, including power allocation and channel assignment, for all users, which, however, leads to mixed integer programs. In the literature, the optimal power allocation has only been found in some special cases, while the joint optimization of power allocation and channel assignment generally requires exhaustive search. In this paper, we investigate resource allocation in downlink NOMA systems. As the main contribution, we analytically characterize the optimal power allocation with given channel assignment over multiple channels under different performance criteria. Specifically, we consider the maximin fairness, weighted sum rate maximization, sum rate maximization with quality of service (QoS) constraints, and energy efficiency maximization with weights or QoS constraints in NOMA systems. We also take explicitly into account the order constraints on the powers of the users on each channel, which are often ignored in the existing works, and show that they have a significant impact on SIC in NOMA systems. Then, we provide the optimal power allocation for the considered criteria in closed or semi-closed form. We also propose a low-complexity efficient method to jointly optimize channel assignment and power allocation in NOMA systems by incorporating the matching algorithm with the optimal power allocation. Simulation results show that the joint resource optimization using our optimal power allocation yields better performance than the existing schemes.