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.

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Determining Image Origin and Integrity Using Sensor Noise

Temporal noise sets the fundamental limit on image sensor performance, especially under low illumination and in video applications. In a CCD image sensor, temporal noise is primarily due to the photodetector shot noise and the output amplifier thermal and 1/f noise. CMOS image sensors suffer from higher noise than CCDs due to the additional pixel and column amplifier transistor thermal and 1/f noise. Noise analysis is further complicated by the time-varying circuit models, the fact that the reset transistor operates in subthreshold during reset, and the nonlinearity of the charge to voltage conversion, which is becoming more pronounced as CMOS technology scales. The paper presents a detailed and rigorous analysis of temporal noise due to thermal and shot noise sources in CMOS active pixel sensor (APS) that takes into consideration these complicating factors. Performing time-domain analysis, instead of the more traditional frequency-domain analysis, we find that the reset noise power due to thermal noise is at most half of its commonly quoted kT/C value. This result is corroborated by several published experimental data including data presented in this paper. The lower reset noise, however, comes at the expense of image lag. We find that alternative reset methods such as overdriving the reset transistor gate or using a pMOS transistor can alleviate lag, but at the expense of doubling the reset noise power. We propose a new reset method that alleviates lag without increasing reset noise.

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Analysis of temporal noise in CMOS photodiode active pixel sensor

Analysis results demonstrate that multiple sampling can achieve consistently higher signal-to-noise ratio at equal or higher dynamic range than using other image sensor dynamic range enhancement schemes such as well capacity adjusting. Implementing multiple sampling, however, requires much higher readout speeds than can be achieved using typical CMOS active pixel sensor (APS). This paper demonstrates, using a 640/spl times/512 CMOS image sensor with 8-b bit-serial Nyquist rate analog-to-digital converter (ADC) per 4 pixels, that pixel-level ADC enables a highly flexible and efficient implementation of multiple sampling to enhance dynamic range. Since pixel values are available to the ADC's at all times, the number and timing of the samples as well as the number of bits obtained from each sample can be freely selected and read out at fast SRAM speeds. By sampling at exponentially increasing exposure times, pixel values with binary floating-point resolution can be obtained. The 640/spl times/512 sensor is implemented in 0.35-/spl mu/m CMOS technology and achieves 10.5/spl times/10.5 /spl mu/m pixel size at 29% fill factor. Characterization techniques and measured quantum efficiency, sensitivity, ADC transfer curve, and fixed pattern noise are presented. A scene with measured dynamic range exceeding 10000:1 is sampled nine times to obtain an image with dynamic range of 65536:1. Limits on achievable dynamic range using multiple sampling are presented.

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A 640/spl times/512 CMOS image sensor with ultra wide dynamic range floating-point pixel-level ADC

The article explains how photo-response nonuniformity (PRNU) of imaging sensors can be used for a variety of important digital forensic tasks, such as device identification, device linking, recovery of processing history, and detection of digital forgeries. The PRNU is an intrinsic property of all digital imaging sensors due to slight variations among individual pixels in their ability to convert photons to electrons. Consequently, every sensor casts a weak noise-like pattern onto every image it takes. This pattern, which plays the role of a sensor fingerprint, is essentially an unintentional stochastic spread-spectrum watermark that survives processing, such as lossy compression or filtering. This tutorial explains how this fingerprint can be estimated from images taken by the camera and later detected in a given image to establish image origin and integrity. Various forensic tasks are formulated as a two-channel hypothesis testing problem approached using the generalized likelihood ratio test. The performance of the introduced forensic methods is briefly illustrated on examples to give the reader a sense of the performance.

Digital image forensics

Fixed pattern noise (FPN) for a CCD sensor is modeled as a sample of a spatial white noise process. This model is, however, not adequate for characterizing FPN in CMOS sensors, since the redout circuitry of CMOS sensors and CCDs are very different. The paper presents a model for CMOS FPN as the sum of two components: a column and a pixel component. Each component is modeled by a first order isotropic autoregressive random process, and each component. Each component is modeled by a first order isotropic autoregressive random process, and each component is assumed to be uncorrelated with the other. The parameters of the processes characterize each component of the FPN and the correlations between neighboring pixels and neighboring columns for a batch of sensor. We show how to estimate the model parameters from a set of measurements, and report estimates for 64 X 64 passive pixel sensor (PPS) and active pixel sensor (APS) test structures implemented in a 0.35 micron CMOS process. High spatial correlations between pixel components were measured for the PPS structures, and between the column components in both PPS and APS. The APS pixel components were uncorrelated.

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Modeling and estimation of FPN components in CMOS image sensors

A set of test structures designed to characterize and compare the performance of CMOS passive and active pixel image sensors is presented. The test structures are deigned so that they can be rapidly ported from one process to another. They are also designed so that individual photodetectors and pixel circuits as well as entire image sensor arrays can be characterized and compared based on: quantum efficiency, spectral response, fixed pattern noise, sensitivity, blooming, input referred read noise, reduction of quantum efficiency caused by silicide/salicide, lag, digital switching noise sensitivity, impact ionization noise sensitivity, dynamic range, and temperature dependency of all measured parameters. Four test chips that include a variety of these structures have been built in two different 0.35 micrometer CMOS processes. The test chips include nineteen types of individual photodetectors and thirty eight types of 64 by 64 pixel arrays. The test methodology and preliminary test results from these chips are presented.

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Test Structures for Characterization and Comparative Analysis of CMOS Image Sensors

3D perovskites are promising to achieve efficient and bright deep‐blue light‐emitting diodes (LEDs), which are required for lighting and display applications. However, the efficiency of deep‐blue 3D perovskite‐based LEDs is limited by high density of defects in perovskites, and their deep‐blue emission is not easy to achieve due to the halide phase separation and low solubility of chloride in precursor solutions. Here, an in situ halide exchange method is developed to achieve deep‐blue 3D perovskites by spin‐coating an organic halide salts solution to treat blue 3D perovskites. It is revealed that the halide‐exchange process is mainly determined by halide ion diffusion targeting a concentration equalization, which leads to homogeneous 3D mixed‐halide perovskites. By further introducing multifunctional organic ammonium halide salts into the exchange solution to passivate defects, high‐quality deep‐blue perovskites with reduced trap density can be obtained. This approach leads to efficient deep‐blue perovskite LEDs with a peak external quantum efficiency (EQE) of 4.6% and a luminance of 1680 cd m−2, which show color coordinates of (0.131, 0.055), very close to the Rec. 2020 blue standard. Moreover, the halide exchange method is bidirectional, and blue perovskite LEDs can be achieved with color coordinates of (0.095, 0.160), exhibiting a high EQE of 11.3%.

In Situ Halide Exchange of Cesium Lead Halide Perovskites for Blue Light‐Emitting Diodes

Owing to their excellent optoelectronic properties, quasi-2D perovskites with self-assembled multiple quantum well (MQW) structures have shown great potential in light-emitting diode (LED) applications. Understanding the correlation between the bulky cation, quantum well assembly, and optoelectronic properties of a quasi-2D perovskite is important. Here, we demonstrate that the dipole moment of the bulky cation can be one of the fundamental factors that controls the distribution and crystallinity of different quantum wells. We find that the bulky cation with a moderate dipole moment leads to moderately distributed well-width MQWs, resulting in a superior device efficiency due to the simultaneous achievement of favorable optical and electronic properties. The peak external quantum efficiency and the maximum luminance of the champion device are 10.8% and 19082 cd m-2, respectively, positioning it among the best-performing quasi-2D green perovskite LEDs without further surface passivation or additive doping. This work provides a perspective on the rational design of bulky cations in quasi-2D perovskite LEDs, which is also essential for the development of other mixed-dimensional perovskite optoelectronic devices.

Hao Min

Papers

Modeling and estimation of FPN components in CMOS image sensors

Test Structures for Characterization and Comparative Analysis of CMOS Image Sensors

In Situ Halide Exchange of Cesium Lead Halide Perovskites for Blue Light‐Emitting Diodes

Molecularly Controlled Quantum Well Width Distribution and Optoelectronic Properties in Quasi-2D Perovskite Light-Emitting Diodes.

Additive treatment yields high-performance lead-free perovskite light-emitting diodes