Hamidreza Sadreazami

Bio: Hamidreza Sadreazami is an academic researcher from University of Ottawa. The author has contributed to research in topics: Contourlet & Digital watermarking. The author has an hindex of 14, co-authored 57 publications receiving 642 citations. Previous affiliations of Hamidreza Sadreazami include Concordia University Wisconsin & Shahid Beheshti University.

A study of multiplicative watermark detection in the contourlet domain using alpha-stable distributions.

Abstract: In the past decade, several schemes for digital image watermarking have been proposed to protect the copyright of an image document or to provide proof of ownership in some identifiable fashion. This paper proposes a novel multiplicative watermarking scheme in the contourlet domain. The effectiveness of a watermark detector depends highly on the modeling of the transform-domain coefficients. In view of this, we first investigate the modeling of the contourlet coefficients by the alpha-stable distributions. It is shown that the univariate alpha-stable distribution fits the empirical data more accurately than the formerly used distributions, such as the generalized Gaussian and Laplacian, do. We also show that the bivariate alpha-stable distribution can capture the across scale dependencies of the contourlet coefficients. Motivated by the modeling results, a blind watermark detector in the contourlet domain is designed by using the univariate and bivariate alpha-stable distributions. It is shown that the detectors based on both of these distributions provide higher detection rates than that based on the generalized Gaussian distribution does. However, a watermark detector designed based on the alpha-stable distribution with a value of its parameter α other than 1 or 2 is computationally expensive because of the lack of a closed-form expression for the distribution in this case. Therefore, a watermark detector is designed based on the bivariate Cauchy member of the alpha-stable family for which α = 1 . The resulting design yields a significantly reduced-complexity detector and provides a performance that is much superior to that of the GG detector and very close to that of the detector corresponding to the best-fit alpha-stable distribution. The robustness of the proposed bivariate Cauchy detector against various kinds of attacks, such as noise, filtering, and compression, is studied and shown to be superior to that of the generalized Gaussian detector.

Multiplicative Watermark Decoder in Contourlet Domain Using the Normal Inverse Gaussian Distribution

Abstract: In recent years, many works on digital image watermarking have been proposed all aiming at protection of the copyright of an image document or authentication of data. This paper proposes a novel watermark decoder in the contourlet domain . It is known that the contourlet coefficients of an image are highly non-Gaussian and a proper distribution to model the statistics of the contourlet coefficients is a heavy-tailed PDF. It has been shown in the literature that the normal inverse Gaussian (NIG) distribution can suitably fit the empirical distribution. In view of this, statistical methods for watermark extraction are proposed by exploiting the NIG as a prior for the contourlet coefficients of images. The proposed watermark extraction approach is developed using the maximum likelihood method based on the NIG distribution. Closed-form expressions are obtained for extracting the watermark bits in both clean and noisy environments. Experiments are performed to verify the robustness of the proposed decoder. The results show that the proposed decoder is superior to other decoders in terms of providing a lower bit error rate. It is also shown that the proposed decoder is highly robust against various kinds of attacks such as noise, rotation, cropping, filtering, and compression.

Fall Detection Using Standoff Radar-Based Sensing and Deep Convolutional Neural Network

Abstract: Automatic fall detection using radar aids in better assisted living and smarter health care. In this brief, a novel time series-based method for detecting fall incidents in human daily activities is proposed. A time series in the slow-time is obtained by summing all the range bins corresponding to fast-time of the ultra wideband radar return signals. This time series is used as input to the proposed deep convolutional neural network for automatic feature extraction. In contrast to other existing methods, the proposed fall detection method relies on multi-level feature learning directly from the radar time series signals. In particular, the proposed method utilizes a deep convolutional neural network for automating feature extraction as well as global maximum pooling technique for enhancing model discriminability. The performance of the proposed method is compared with that of the state-of-the-art, such as recurrent neural network, multi-layer perceptron, and dynamic time warping techniques. The results demonstrate that the proposed fall detection method outperforms the other methods in terms of higher accuracy, precision, sensitivity, and specificity values.

Distributed-Graph-Based Statistical Approach for Intrusion Detection in Cyber-Physical Systems

Abstract: Cyber-physical systems have recently emerged in several practical engineering applications where security and privacy are of paramount importance. This motivated the paper and a recent surge of interest in development of innovative and novel anomaly and intrusion detection technologies. This paper proposes a novel distributed blind intrusion detection framework by modeling sensor measurements as the target graph-signal and utilizing the statistical properties of the graph-signal for intrusion detection. To fully take into account the underlying network structure, the graph similarity matrix is constructed using both the data measured by the sensors and sensors’ proximity resulting in a data-adaptive and structure-aware monitoring solution. In the proposed supervised detection framework, the magnitude of the captured data is modeled by Gaussian Markov random field and the corresponding precision matrix is estimated by learning a graph Laplacian matrix from sensor measurements adaptively. The proposed intrusion detection methodology is designed based on a modified Bayesian likelihood ratio test and the closed-form expressions are derived for the test statistic. Finally, temporal analysis of the network behavior is established by computing the Bhattacharyya distance between the measurement distributions at the consecutive time instants. Experiments are conducted to evaluate the performance of the proposed method and to compare it with that of the state-of-the-art methods. The results show that the proposed intrusion detection framework provides a detection performance superior to those provided by the other existing schemes.

A Robust Image Watermarking Scheme Using Local Statistical Distribution in the Contourlet Domain

Abstract: Data security is a main concern in everyday data transmissions in the Internet. A possible solution to guarantee a secure and legitimate transaction is via hiding a piece of tractable information into the multimedia signal, i.e., watermarking. This brief proposes a new multiplicative image watermarking scheme in the contourlet domain by taking into account the local statistical properties and inter-scale dependencies of the contourlet coefficients of images. Although the contourlet coefficients are non-Gaussian within a sub-band, their local distribution fits the Gaussian distribution very well. In addition, it is known that there exist across-scale dependencies among these coefficients. In view of this, we propose the use of bivariate Gaussian (BVG) distribution to model the distribution of the contourlet coefficients. Motivated by the modeling results, an optimum blind watermark decoder is designed in the contourlet domain using the maximum likelihood method. By means of carrying out a number of experiments, the performance of the proposed decoder is investigated with regard to the bit error rate and compared to other decoders. It will be shown that the proposed decoder built upon the BVG model is superior to other decoders in terms of rate of error. It will also be shown that the proposed decoder provides higher robustness in comparison to other decoders in presence of attacks such as filtering, compression, cropping, scaling, and noise.

Survey of intrusion detection systems: techniques, datasets and challenges

Abstract: Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade the credibility of security services, e.g. data confidentiality, integrity, and availability. Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats, which can be broadly classified into Signature-based Intrusion Detection Systems (SIDS) and Anomaly-based Intrusion Detection Systems (AIDS). This survey paper presents a taxonomy of contemporary IDS, a comprehensive review of notable recent works, and an overview of the datasets commonly used for evaluation purposes. It also presents evasion techniques used by attackers to avoid detection and discusses future research challenges to counter such techniques so as to make computer systems more secure.

Intelligent metasurface imager and recognizer.

Abstract: There is an increasing need to remotely monitor people in daily life using radio-frequency probe signals. However, conventional systems can hardly be deployed in real-world settings since they typically require objects to either deliberately cooperate or carry a wireless active device or identification tag. To accomplish complicated successive tasks using a single device in real time, we propose the simultaneous use of a smart metasurface imager and recognizer, empowered by a network of artificial neural networks (ANNs) for adaptively controlling data flow. Here, three ANNs are employed in an integrated hierarchy, transforming measured microwave data into images of the whole human body, classifying specifically designated spots (hand and chest) within the whole image, and recognizing human hand signs instantly at a Wi-Fi frequency of 2.4 GHz. Instantaneous in situ full-scene imaging and adaptive recognition of hand signs and vital signs of multiple non-cooperative people were experimentally demonstrated. We also show that the proposed intelligent metasurface system works well even when it is passively excited by stray Wi-Fi signals that ubiquitously exist in our daily lives. The reported strategy could open up a new avenue for future smart cities, smart homes, human-device interaction interfaces, health monitoring, and safety screening free of visual privacy issues. Combining radio-frequency imaging with artificial intelligence could make it easier for computers to interact with individuals using non-verbal cues, such as sign language. Lianlin Li from Peking University in Beijing, China and Tie Jun Cui from Southeast University in Nanjing, China, and co-workers fabricated a meter-scale flat panel containing ‘meta-atoms’, tiny electronic devices that manipulate the phases of light waves, arranged in a grid-like pattern. By emitting microwave signals or manipulating stray Wi-Fi signals and detecting echoes bounced back, the metasurface can collect high-resolution imaging data on multiple non-cooperative subjects, even those behind solid walls. The teams fed the microwave data to a series of artificial intelligence algorithms that first identify human shapes, modify signal distributions to better focus on specific body parts, and recognize people's hand signs and vital signs . Experiments showed this setup could continuously monitor hand signals and breathing, even using stray Wi-Fi signals that ubiquitously exist in the daily lives.

A novel blind robust image watermarking in DCT domain using inter-block coefficient correlation

Abstract: This paper presents a novel blind watermarking algorithm in DCT domain using the correlation between two DCT coefficients of adjacent blocks in the same position. One DCT coefficient of each block is modified to bring the difference from the adjacent block coefficient in a specified range. The value used to modify the coefficient is obtained by finding difference between DC and median of a few low frequency AC coefficients and the result is normalized by DC coefficient. The proposed watermarking algorithm is tested for different attacks. It shows very good robustness under JPEG image compression as compared to existing one and also good quality of watermark is extracted by performing other common image processing operations like cropping, rotation, brightening, sharpening, contrast enhancement etc.

Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images

Abstract: Despite the remarkable advances in visual saliency analysis for natural scene images (NSIs), salient object detection (SOD) for optical remote sensing images (RSIs) still remains an open and challenging problem. In this paper, we propose an end-to-end Dense Attention Fluid Network (DAFNet) for SOD in optical RSIs. A Global Context-aware Attention (GCA) module is proposed to adaptively capture long-range semantic context relationships, and is further embedded in a Dense Attention Fluid (DAF) structure that enables shallow attention cues flow into deep layers to guide the generation of high-level feature attention maps. Specifically, the GCA module is composed of two key components, where the global feature aggregation module achieves mutual reinforcement of salient feature embeddings from any two spatial locations, and the cascaded pyramid attention module tackles the scale variation issue by building up a cascaded pyramid framework to progressively refine the attention map in a coarse-to-fine manner. In addition, we construct a new and challenging optical RSI dataset for SOD that contains 2,000 images with pixel-wise saliency annotations, which is currently the largest publicly available benchmark. Extensive experiments demonstrate that our proposed DAFNet significantly outperforms the existing state-of-the-art SOD competitors. https://github.com/rmcong/DAFNet_TIP20