(b) Write out the equations for the time dependence of ρ11, ρ22, ρ12 and ρ21 assuming that a light field interaction V = -μ12Ee iωt + c.c. couples only levels |1> and |2>, and that the excited levels exhibit spontaneous decay. (8 marks) (c) Under steady-state conditions, find the ratio of populations in states |2> and |3>. (3 marks) (d) Find the slowly varying amplitude ̃ ρ 12 of the polarization ρ12 = ̃ ρ 12e iωt . (6 marks) (e) In the limiting case that no decay is possible from intermediate level |3>, what is the ground state population ρ11(∞)? (2 marks) 2. (15 marks total) In a 2-level atom system subjected to a strong field, dressed states are created in the form |D1(n)> = sin θ |1,n> + cos θ |2,n-1> |D2(n)> = cos θ |1,n> sin θ |2,n-1>

The Quantum Theory of Light

A review of content-based image retrieval systems in medical applications—clinical benefits and future directions

In this paper, we propose a new method for the problem of digital camera identification from its images based on the sensor's pattern noise. For each camera under investigation, we first determine its reference pattern noise, which serves as a unique identification fingerprint. This is achieved by averaging the noise obtained from multiple images using a denoising filter. To identify the camera from a given image, we consider the reference pattern noise as a spread-spectrum watermark, whose presence in the image is established by using a correlation detector. Experiments on approximately 320 images taken with nine consumer digital cameras are used to estimate false alarm rates and false rejection rates. Additionally, we study how the error rates change with common image processing, such as JPEG compression or gamma correction.

/pdf/digital-camera-identification-from-sensor-pattern-noise-2zx31ufisg.pdf

Digital camera identification from sensor pattern noise

In this paper, we provide a unified framework for identifying the source digital camera from its images and for revealing digitally altered images using photo-response nonuniformity noise (PRNU), which is a unique stochastic fingerprint of imaging sensors. The PRNU is obtained using a maximum-likelihood estimator derived from a simplified model of the sensor output. Both digital forensics tasks are then achieved by detecting the presence of sensor PRNU in specific regions of the image under investigation. The detection is formulated as a hypothesis testing problem. The statistical distribution of the optimal test statistics is obtained using a predictor of the test statistics on small image blocks. The predictor enables more accurate and meaningful estimation of probabilities of false rejection of a correct camera and missed detection of a tampered region. We also include a benchmark implementation of this framework and detailed experimental validation. The robustness of the proposed forensic methods is tested on common image processing, such as JPEG compression, gamma correction, resizing, and denoising.

/pdf/determining-image-origin-and-integrity-using-sensor-noise-375kg5mshh.pdf

Determining Image Origin and Integrity Using Sensor Noise

This survey describes the current state-of-the-art in the development of automated visual surveillance systems so as to provide researchers in the field with a summary of progress achieved to date and to identify areas where further research is needed. The ability to recognise objects and humans, to describe their actions and interactions from information acquired by sensors is essential for automated visual surveillance. The increasing need for intelligent visual surveillance in commercial, law enforcement and military applications makes automated visual surveillance systems one of the main current application domains in computer vision. The emphasis of this review is on discussion of the creation of intelligent distributed automated surveillance systems. The survey concludes with a discussion of possible future directions.

/pdf/intelligent-distributed-surveillance-systems-a-review-1jp146ld4r.pdf

Intelligent distributed surveillance systems: a review

From the court we were asked whether it is possible to determine if an image has been made with a specific digital camera. This question has to be answered in child pornography cases, where evidence is needed that a certain picture has been made with a specific camera. We have looked into different methods of examining the cameras to determine if a specific image has been made with a camera: defects in CCDs, file formats that are used, noise introduced by the pixel arrays and watermarking in images used by the camera manufacturer.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

/pdf/methods-for-identification-of-images-acquired-with-digital-11mymn0rkr.pdf

Methods for identification of images acquired with digital cameras

The image-database REBEZO for shoeprints with developments on automatic classification of shoe outsole designs

The Electric Network Frequency (ENF) Criterion is a recently developed forensic technique for determining the time of recording of digital audio recordings, by matching the ENF pattern from a questioned recording with an ENF pattern database. In this paper we discuss its inherent limitations in the case of short --- i.e., less than 10 minutes in duration --- digital audio recordings. We also present a matching procedure based on the correlation coefficient, as a more robust alternative to squared error matching.

Using the ENF Criterion for Determining the Time of Recording of Short Digital Audio Recordings

Source video camera identification for multiply compressed videos originating from YouTube

In this paper, the importance of modern technology in forensic investigations is discussed. Recent technological developments are creating new possibilities to perform robust scientific measurements and studies outside the controlled laboratory environment. The benefits of real-time, on-site forensic investigations are manifold and such technology has the potential to strongly increase the speed and efficacy of the criminal justice system. However, such benefits are only realized when quality can be guaranteed at all times and findings can be used as forensic evidence in court. At the Netherlands Forensic Institute, innovation efforts are currently undertaken to develop integrated forensic platform solutions that allow for the forensic investigation of human biological traces, the chemical identification of illicit drugs and the study of large amounts of digital evidence. These platforms enable field investigations, yield robust and validated evidence and allow for forensic intelligence and targeted use of expert capacity at the forensic institutes. This technological revolution in forensic science could ultimately lead to a paradigm shift in which a new role of the forensic expert emerges as developer and custodian of integrated forensic platforms.

/pdf/the-interface-between-forensic-science-and-technology-how-4l1it7cns4.pdf

The interface between forensic science and technology: how technology could cause a paradigm shift in the role of forensic institutes in the criminal justice system.

Zeno Geradts

Papers

Methods for identification of images acquired with digital cameras

The image-database REBEZO for shoeprints with developments on automatic classification of shoe outsole designs

Using the ENF Criterion for Determining the Time of Recording of Short Digital Audio Recordings

Source video camera identification for multiply compressed videos originating from YouTube

The interface between forensic science and technology: how technology could cause a paradigm shift in the role of forensic institutes in the criminal justice system.