On the Internet, humans must repeatedly identify themselves to gain access to information or to use services. To check whether a request is sent by a human being and not by a computer, a task must be solved. These tasks are called CAPTCHAs and are designed to be easy for most people to solve and at the same time as unsolvable as possible for a computer. In the context of automated OSINT, which requires automatic solving of CAPTCHAs, we investigate the solving of audio CAPTCHAs. For this purpose, a program is written that integrates two common speech-to-text methods. The program achieves very good results and reaches an accuracy of about 81 percent. As CAPTCHAs are also an important tool for Internet access security, we also use the results of our attack to make suggestions for improving the security of these CAPTCHAs. We compares human listeners with computers and reveal weaknesses of audio CAPTCHAs.
The Noise Power Spectrum (NPS) is a standard measure for image capture system noise. It is derived traditionally from captured uniform luminance patches that are unrepresentative of pictorial scene signals. Many contemporary capture systems apply nonlinear content-aware signal processing, which renders their noise scene-dependent. For scene-dependent systems, measuring the NPS with respect to uniform patch signals fails to characterize with accuracy: i) system noise concerning a given input scene, ii) the average system noise power in real-world applications. The sceneand- process-dependent NPS (SPD-NPS) framework addresses these limitations by measuring temporally varying system noise with respect to any given input signal. In this paper, we examine the scene-dependency of simulated camera pipelines in-depth by deriving SPD-NPSs from fifty test scenes. The pipelines apply either linear or non-linear denoising and sharpening, tuned to optimize output image quality at various opacity levels and exposures. Further, we present the integrated area under the mean of SPD-NPS curves over a representative scene set as an objective system noise metric, and their relative standard deviation area (RSDA) as a metric for system noise scene-dependency. We close by discussing how these metrics can also be computed using scene-and-processdependent Modulation Transfer Functions (SPD-MTF).
This paper describes a CMOS image sensor (CIS) horizontal band noise reduction methodology considering on-chip and offchip camera module PCB design parameters. The horizontal band noise is a crucial issue for high quality camera of modern smartphone applications. This paper discusses CIS horizontal band noise mechanism and proposes the solution by optimization of design factors in CIS and camera module. Analog ground impedance value and bias voltage condition of pixel array transfer gate have been found to be effective optimization parameters. Through the real experimental data, we proved that proposed solution is instrumental in reducing the horizontal band noise.