The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

Recent studies in computer vision have shown that, while practically invisible to a human observer, skin color changes due to blood flow can be captured on face videos and, surprisingly, be used to estimate the heart rate (HR). While considerable progress has been made in the last few years, still many issues remain open. In particular, state of-the-art approaches are not robust enough to operate in natural conditions (e.g. in case of spontaneous movements, facial expressions, or illumination changes). Opposite to previous approaches that estimate the HR by processing all the skin pixels inside a fixed region of interest, we introduce a strategy to dynamically select face regions useful for robust HR estimation. Our approach, inspired by recent advances on matrix completion theory, allows us to predict the HR while simultaneously discover the best regions of the face to be used for estimation. Thorough experimental evaluation conducted on public benchmarks suggests that the proposed approach significantly outperforms state-of the-art HR estimation methods in naturalistic conditions.

/pdf/self-adaptive-matrix-completion-for-heart-rate-estimation-34gc3nht50.pdf

Self-Adaptive Matrix Completion for Heart Rate Estimation from Face Videos under Realistic Conditions

Vital signs such as pulse rate and breathing rate are currently measured using contact probes. But, non-contact methods for measuring vital signs are desirable both in hospital settings (e.g. in NICU) and for ubiquitous in-situ health tracking (e.g. on mobile phone and computers with webcams). Recently, camera-based non-contact vital sign monitoring have been shown to be feasible. However, camera-based vital sign monitoring is challenging for people with darker skin tone, under low lighting conditions, and/or during movement of an individual in front of the camera. In this paper, we propose distancePPG, a new camera-based vital sign estimation algorithm which addresses these challenges. DistancePPG proposes a new method of combining skin-color change signals from different tracked regions of the face using a weighted average, where the weights depend on the blood perfusion and incident light intensity in the region, to improve the signal-to-noise ratio (SNR) of camera-based estimate. One of our key contributions is a new automatic method for determining the weights based only on the video recording of the subject. The gains in SNR of camera-based PPG estimated using distancePPG translate into reduction of the error in vital sign estimation, and thus expand the scope of camera-based vital sign monitoring to potentially challenging scenarios. Further, a dataset will be released, comprising of synchronized video recordings of face and pulse oximeter based ground truth recordings from the earlobe for people with different skin tones, under different lighting conditions and for various motion scenarios.

DistancePPG: Robust non-contact vital signs monitoring using a camera

Social media security and trustworthiness: Overview and new direction

Digital asset management systems (DAMS) generally handle media data in a compressed and encrypted form. It is sometimes necessary to watermark these compressed encrypted media items in the compressed-encrypted domain itself for tamper detection or ownership declaration or copyright management purposes. It is a challenge to watermark these compressed encrypted streams as the compression process would have packed the information of raw media into a low number of bits and encryption would have randomized the compressed bit stream. Attempting to watermark such a randomized bit stream can cause a dramatic degradation of the media quality. Thus it is necessary to choose an encryption scheme that is both secure and will allow watermarking in a predictable manner in the compressed encrypted domain. In this paper, we propose a robust watermarking algorithm to watermark JPEG2000 compressed and encrypted images. The encryption algorithm we propose to use is a stream cipher. While the proposed technique embeds watermark in the compressed-encrypted domain, the extraction of watermark can be done in the decrypted domain. We investigate in detail the embedding capacity, robustness, perceptual quality and security of the proposed algorithm, using these watermarking schemes: Spread Spectrum (SS), Scalar Costa Scheme Quantization Index Modulation (SCS-QIM), and Rational Dither Modulation (RDM).

Robust Watermarking of Compressed and Encrypted JPEG2000 Images

In this paper, we propose a novel video forgery detection technique to detect the spatial and temporal copy paste tampering. It is a challenge to detect the spatial and temporal copy-paste tampering in videos as the forged patch may drastically vary in terms of size, compression rate and compression type (I, B or P) or other changes such as scaling and filtering. In our proposed algorithm, the copy-paste forgery detection is based on Histogram of Oriented Gradients (HOG) feature matching and video compression properties. The benefit of using HOG features is that they are robust against various signal processing manipulations. The experimental results show that the forgery detection performance is very effective. We also compare our results against a popular copy-paste forgery detection algorithm. In addition, we analyze the experimental results for different forged patch sizes under varying degree of modifications such as compression, scaling and filtering.

Video forgery detection using HOG features and compression properties

Multiparty multilevel digital rights management (DRM) architecture involving several levels of distributors in between an owner and a consumer has been suggested as an alternative business model to the traditional two-party (buyer-seller) DRM architecture for digital content delivery. In the two-party DRM architecture, cryptographic techniques are used for secure delivery of the content, and watermarking techniques are used for protecting the rights of the seller and the buyer. The cryptographic protocols used in the two-party case for secure content delivery can be directly applied to the multiparty multilevel case. However, the watermarking protocols used in the two-party case may not directly carry over to the multiparty multilevel case, as it needs to address the simultaneous security concerns of multiple parties such as the owner, multiple levels of distributors, and consumers. Towards this, in this paper, we propose a joint digital watermarking scheme using Chinese remainder theorem for the multiparty multilevel DRM architecture. In the proposed scheme, watermark information is jointly created by all the parties involved; then a watermark signal is generated out of it and embedded into the content. This scheme takes care of the security concerns of all parties involved. Further, in the event of finding an illegal copy of the content, the violator(s) can be traced back.

/pdf/joint-watermarking-scheme-for-multiparty-multilevel-drm-2r7yr20dcu.pdf

Joint Watermarking Scheme for Multiparty Multilevel DRM Architecture

Hypertension (high blood pressure) is the leading cause for increasing number of premature deaths due to cardiovascular diseases. Continuous hypertension screening seems to be a promising approach in order to take appropriate steps to alleviate hypertension-related diseases. Many studies have shown that physiological signal like Photoplethysmogram (PPG) can be reliably used for predicting the Blood Pressure (BP) and Heart Rate (HR). However, the existing approaches use a transmission or reflective type wearable sensor to collect the PPG signal. These sensors are bulky and mostly require an assistance of a trained medical practitioner; which preclude these approaches from continuous BP monitoring outside the medical centers. In this paper, we propose a novel touchless approach that predicts BP and HR using the face video based PPG. Since the facial video can easily be captured using a consumer grade camera, this approach is a convenient way for continuous hypertension monitoring outside the medical centers. The approach is validated using the face video data collected in our lab, with the ground truth BP and HR measured using a clinically approved BP monitor OMRON HBP1300. Accuracy of the method is measured in terms of normalized mean square error, mean absolute error and error standard deviation; which complies with the standards mentioned by Association for the Advancement of Medical Instrumentation. Two-tailed dependent sample t-test is also conducted to verify that there is no statistically significant difference between the BP and HR predicted using the proposed approach and the BP and HR measured using OMRON.

Face video based touchless blood pressure and heart rate estimation

Visible-infrared person re-identification (RGB-IR ReID) is extremely important for the surveillance applications under poor illumination conditions. Since the difference in the feature representations not only lies in the person’ pose, viewpoint or illumination variations, but also comes from huge spectrum discrepancy, the task becomes practically very challenging. Existing RGB-IR ReID models focus on bridging the gap between RGB and IR images through shared feature embedding, subspace learning or via adversarial learning. However, these methods do not explicitly disregard the spectrum information which is otherwise irrelevant for ReID. Further, adversarial learning methods has less promising convergence. This motivates us to design a non-adversarial and fast disentanglement method to disentangle the spectrum information while learning the identity discriminative features. To extract these features, we propose a novel network with disentanglement loss which can distill identity features and dispel spectrum features. Our network has two branches, spectrum dispelling and spectrum distilling branch. On spectrum dispelling branch, we apply identification loss to learn the identity related and spectrum disentangled features. On spectrum distilling branch, we apply an identity-dispeller loss to fool the identity classifier so that it primarily learns spectrum related information. The entire network is trained in an end-to-end manner, which minimizes spectrum information and maximizes invariant identity relevant information at spectrum dispelling branch. Extensive experiments on existing datasets demonstrate the superior performance of our approach compared to the state-of-the-art.

A. V. Subramanyam

Papers

Robust Watermarking of Compressed and Encrypted JPEG2000 Images

Video forgery detection using HOG features and compression properties

Joint Watermarking Scheme for Multiparty Multilevel DRM Architecture

Face video based touchless blood pressure and heart rate estimation

SDL: Spectrum-Disentangled Representation Learning for Visible-Infrared Person Re-Identification