Background Subtraction based on Geometric - K mean Algorithm

Panda, Susmita; Agrawal, Aman

doi:10.1109/odicon50556.2021.9428949

Cited by 1 publication

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of semantic segmentation can be helpful in determining important areas; however, it cannot be the basic method. A similar effect can also be obtained by background estimation methods [ 31 ], where the use of background estimation techniques seems to be a good method for improving the efficiency of the detection of transmission areas in the proposed method [ 32 ]. The support of the presented method, with the help of semantic segmentation and background estimation, is probably a good starting point for further work in the discussed topic.…”

Section: Discussionmentioning

confidence: 68%

Video Sequence Segmentation Based on K-Means in Air-Gap Data Transmission for a Cluttered Environment

Mazurek

Bak²

2023

Sensors

View full text Add to dashboard Cite

An air gap is a technique that increases the security of information systems. The use of unconventional communication channels allows for obtaining communication that is of interest to the attacker as well as to cybersecurity engineers. One of the very dangerous forms of attack is the use of computer screen brightness modulation, which is not visible to the user but can be observed from a distance by the attacker. Once infected, the computer can transmit data over long distances. Even in the absence of direct screen visibility, transmission can be realized by analyzing the modulated reflection of the monitor’s afterglow. The paper presents a new method for the automatic segmentation of video sequences to retrieve the transmitted data that does not have the drawbacks of the heretofore known method of growth (filling) based on an analysis of adjacent pixels. A fast camera operating at 380 fps was used for image acquisition. The method uses the characteristics of the amplitude spectrum for individual pixels, which is specific to the light sources in the room, and clustering with the k-means algorithm to group pixels into larger areas. Then, using the averaging of values for individual areas, it is possible to recover the 2-PAM (pulse-amplitude modulation) signal even at a 1000 times greater level of interference in the area to the transmitted signal, as shown in the experiments. The method does not require high-quality lenses.

show abstract

Section: Discussionmentioning

confidence: 68%