scholarly journals The Optimization Analysis for the Original and Manipulation Identification of Illegally Filmed Images

2021 ◽  
Vol 11 (11) ◽  
pp. 5220
Author(s):  
Soohyeon Choi ◽  
Dohoon Kim
Keyword(s):  
Social Media ◽  
Mobile Device ◽  
Detection Rate ◽  
Precise Measurement ◽  
Detection Method ◽  
Optimization Analysis ◽  
Storage Devices ◽  
Digital Forensic ◽  
Attribute Information ◽  
Measurement Approach

Illegally filmed images, the sharing of non-consensually filmed images over social media, and the secret recording and distribution of celebrity images are increasing. To catch distributors of illegally filmed images, many investigation techniques based on an analysis of the file attribute information of the original images have been introduced. As forensic science advances, various types of anti-forensic technologies are being produced, requiring investigators to open and analyze all videos from the suspect’s storage devices, raising the question of the invasion of privacy during the investigation. The suspect can even file a lawsuit, which makes issuing a warrant and conducting an investigation difficult. Thus, it is necessary to detect the original and manipulated images without needing to directly go through multiple videos. We propose an optimization analysis and detection method for extracting original and manipulated images from seized devices of suspects. In addition, to increase the detection rate of both original and manipulated images, we suggest a precise measurement approach for comparative thresholds. Thus, the proposed method is a new digital forensic methodology for comparing and identifying original and manipulated images accurately without the need for opening videos individually in a suspect’s mobile device.

Examining the accuracy of estimated smartphone use: How well-being and usage level predict discrepancies between estimated and actual use

2019 ◽  
Author(s):  
Craig Sewall ◽  
Daniel Rosen ◽  
Todd M. Bear
Keyword(s):  
Public Health ◽  
Social Media ◽  
Life Satisfaction ◽  
Mobile Device ◽  
Screen Time ◽  
Well Being ◽  
Actual Use ◽  
Smartphone Use ◽  
Usage Level

The increasing ubiquity of mobile device and social media (SM) use has generated a substantial amount of research examining how these phenomena may impact public health. Prior studies have found that mobile device and SM use are associated with various aspects of well-being. However, a large portion of these studies relied upon self-reported estimates to measure amount of use, which can be inaccurate. Utilizing Apple’s “Screen Time” application to obtain actual iPhone and SM use data, the current study examined the accuracy of self-reported estimates, how inaccuracies bias relationships between use and well-being (depression, loneliness, and life satisfaction), and the degree to which inaccuracies were predicted by levels of well-being. Among a sample of 393 iPhone users, we found that: a.) participants misestimated their weekly overall iPhone and SM use by 22.1 and 16.6 hours, respectively; b.) the correlations between estimated use and well-being variables were consistently stronger than the correlations between actual use and well-being variables; and c.) the amount of inaccuracy in estimated use is associated with levels of participant well-being as well as amount of use. These findings suggest that estimates of device/SM use may be biased by factors that are fundamental to the relationships being investigated. **This manuscript is currently under review**

SensePlace3: a geovisual framework to analyze place–time–attribute information in social media

2017 ◽  
Vol 45 (5) ◽  
pp. 420-437 ◽  
Author(s):  
Scott Pezanowski ◽  
Alan M MacEachren ◽  
Alexander Savelyev ◽  
Anthony C Robinson
Keyword(s):  
Social Media ◽  
Attribute Information

scholarly journals A Deep Learning Approach of Vehicle Multitarget Detection from Traffic Video

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xun Li ◽  
Yao Liu ◽  
Zhengfan Zhao ◽  
Yue Zhang ◽  
Li He
Keyword(s):  
Detection Rate ◽  
Detection Method ◽  
Training Model ◽  
Learning Approach ◽  
Flow State ◽  
False Detection Rate ◽  
False Detection ◽  
Road Vehicles ◽  
Traffic Video ◽  
Flow States

Vehicle detection is expected to be robust and efficient in various scenes. We propose a multivehicle detection method, which consists of YOLO under the Darknet framework. We also improve the YOLO-voc structure according to the change of the target scene and traffic flow. The classification training model is obtained based on ImageNet and the parameters are fine-tuned according to the training results and the vehicle characteristics. Finally, we obtain an effective YOLO-vocRV network for road vehicles detection. In order to verify the performance of our method, the experiment is carried out on different vehicle flow states and compared with the classical YOLO-voc, YOLO 9000, and YOLO v3. The experimental results show that our method achieves the detection rate of 98.6% in free flow state, 97.8% in synchronous flow state, and 96.3% in blocking flow state, respectively. In addition, our proposed method has less false detection rate than previous works and shows good robustness.

scholarly journals A Soft-YoloV4 for High-Performance Head Detection and Counting

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3096
Author(s):  
Zhen Zhang ◽  
Shihao Xia ◽  
Yuxing Cai ◽  
Cuimei Yang ◽  
Shaoning Zeng
Keyword(s):  
High Performance ◽  
Detection Rate ◽  
Detection Method ◽  
State Of The Art ◽  
Counting Method ◽  
People Counting ◽  
Detection Model ◽  
Detection And Counting ◽  
Head Detection ◽  
Ap Value

Blockage of pedestrians will cause inaccurate people counting, and people’s heads are easily blocked by each other in crowded occasions. To reduce missed detections as much as possible and improve the capability of the detection model, this paper proposes a new people counting method, named Soft-YoloV4, by attenuating the score of adjacent detection frames to prevent the occurrence of missed detection. The proposed Soft-YoloV4 improves the accuracy of people counting and reduces the incorrect elimination of the detection frames when heads are blocked by each other. Compared with the state-of-the-art YoloV4, the AP value of the proposed head detection method is increased from 88.52 to 90.54%. The Soft-YoloV4 model has much higher robustness and a lower missed detection rate for head detection, and therefore it dramatically improves the accuracy of people counting.

scholarly journals Bacterial colonies detecting and counting based on enhanced CNN detection method

2021 ◽  
Vol 233 ◽  
pp. 02012
Author(s):  
Shousheng Liu ◽  
Zhigang Gai ◽  
Xu Chai ◽  
Fengxiang Guo ◽  
Mei Zhang ◽  
...  
Keyword(s):  
Target Detection ◽  
Error Detection ◽  
Detection Rate ◽  
Detection Method ◽  
Difficult Problem ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Detection Model ◽  
Confidence Threshold ◽  
Small Targets

Bacterial colonies detecting and counting is tedious and time-consuming work. Fortunately CNN (convolutional neural network) detection methods are effective for target detection. The bacterial colonies are a kind of small targets, which have been a difficult problem in the field of target detection technology. This paper proposes a small target enhancement detection method based on double CNNs, which can not only improve the detection accuracy, but also maintain the detection speed similar to the general detection model. The detection method uses double CNNs. The first CNN uses SSD_MOBILENET_V1 network with both target positioning and target recognition functions. The candidate targets are screened out with a low confidence threshold, which can ensure no missing detection of small targets. The second CNN obtains candidate target regions according to the first round of detection, intercepts image sub-blocks one by one, uses the MOBILENET_V1 network to filter out targets with a higher confidence threshold, which can ensure good detection of small targets. Through the two-round enhancement detection method has been transplanted to the embedded platform NVIDIA Jetson AGX Xavier, the detection accuracy of small targets is significantly improved, and the target error detection rate and missed detection rate are reduced to less than 1%.

scholarly journals Social media: an exploration of the outcomes of social media use for homeless youth

2021 ◽  
Author(s):  
Jennifer Donath
Keyword(s):  
Social Media ◽  
Young Adults ◽  
Mobile Device ◽  
Homeless Youth ◽  
Qualitative Interviews ◽  
Daily Basis ◽  
Future Research ◽  
The Internet ◽  
The Impact ◽  
The City

The purpose of this project was to explore how young adults are experiencing relationships when using social media. Using a qualitative design young adults between the ages of 16-25 were asked questions about their experience with social media in the city of Toronto. Qualitative interviews were conducted with three homeless participants who used social media and had access to the internet and a mobile device. The researcher analyzed the data by looking for themes within the participant’s answers. Discussion focuses on the impact of social media, their experiences as a homeless youth and the interaction with social media on a daily basis. The findings also suggest future research for technology amongst homeless youth

scholarly journals On the Use of Single-, Dual-, and Quad-Polarimetric SAR Observation for Landslide Detection

2019 ◽  
Vol 8 (9) ◽  
pp. 384 ◽  
Author(s):  
Park ◽  
Lee
Keyword(s):  
Remote Sensing ◽  
Detection Rate ◽  
Detection Method ◽  
Incidence Angle ◽  
Accuracy Assessment ◽  
Temporal Variations ◽  
The Other ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Simple Change

Remote sensing technologies, particularly with Synthetic Aperture Radar (SAR) system, can provide timely and critical information to assess landslide distributions over large areas. Most space-borne SAR systems have been operating in different polarimetric modes to meet various operational requirements. This study aims to discuss how much detectability can be expected in the landslide map produced from the single-, dual-, and quad-polarization modes of observation. The experimental analysis of the characteristic changes of PALSAR-2 signals showed that quad-polarization parameters indicating signal depolarization properties revealed noticeable landslide-induced temporal changes for all local incidence angle ranges. To produce a landslide map, a simple change detection method based on characteristic scattering properties of landslide areas was proposed. The accuracy assessment results showed that the depolarization parameters, such as the co-pol coherence and polarizing contribution, can identify areas affected by landslides with a detection rate of 60%, and a false-alarm rate of 5%. On the other hand, the single- or dual-pol parameters can only be expected to provide half the accuracy with significant false-alarms in areas with temporal variations independent of landslides.

scholarly journals “Do I Look Like My Selfie?”: Filters and the Digital-Forensic Gaze

2020 ◽  
Vol 6 (4) ◽  
pp. 205630512095518
Author(s):  
Christine Lavrence ◽  
Carolina Cambre
Keyword(s):  
Social Media ◽  
Focus Groups ◽  
A Priori ◽  
Photo Elicitation ◽  
Digital Forensic ◽  
Digital Ecosystem ◽  
Digital Environments ◽  
Dominant Discourses ◽  
Affective Dimensions

Filtered faces are some of the most heavily engaged photos on social media. The vast majority of literature on selfies have focused on self-reported practices of creating and posting selfies and how subjects view themselves, but research on using filters and the kinds of looking filter provoke is underexplored. Part of a larger project, this analysis draws from a study using photo-elicitation techniques to discuss selfie filters with 12 focus groups, exploring the dominant discourses of cis-gendered looking within digital sociality. We explore how participants edit their selfies, imagine potential audiences, interact with, and perceive the filtering behaviors of others, asking what the “work” of filters is, visually and socially. We probe the kinds of discourses filters participate in, and their gendered and affective dimensions. Our focus groups indicate that when looking at the selfies of others there is often an a priori assumption that filtering has been applied, whether conspicuously or not, to the extent that visual tune-ups have become central to the genre itself. As such, we explore the ambivalence and anxiety about authenticity that filters produce, as well as the intense looking practices aimed at decoding the legitimacy of images. We posit that filters are part of a digital ecosystem that demands an intensification of looking practices, which produce and enhance specific forms of objectification directed toward selves and others within digital environments.

A Research on Real-Time Hand-Detection Technology Oriented to the Somatic Games

2013 ◽  
Vol 748 ◽  
pp. 999-1002 ◽  
Author(s):  
Ren Chen ◽  
Hui Li
Keyword(s):  
Real Time ◽  
Failure Rate ◽  
Skin Color ◽  
Detection Rate ◽  
Detection Method ◽  
Hand Detection ◽  
Key Technology ◽  
Video Frames ◽  
Detection Technology ◽  
Adaboost Classifier

Hand-detection is a key technology to the somatic games. In this paper, we present a real-time hand-detection method based on Adaboost and skin-color characteristic. By processing the video frames with Adaboost classifier, we abstract the target regions which may contain the hand gestures. Then a filter based on skin color is proposed to select the correct regions. The best detection rate reaches above 89% with an acceptable failure rate and misjudgment rate. Experimental results show that this method is a lightweight and rapid approach to implement real-time hand detection in somatic games.

Improved FAST Corner Detection Based on Harris Algorithm for Chinese Characters

2013 ◽  
Vol 850-851 ◽  
pp. 767-770 ◽  
Author(s):  
Na Yao ◽  
Tie Cheng Bai ◽  
Jie Chen
Keyword(s):  
Feature Extraction ◽  
Fast Algorithm ◽  
Detection Rate ◽  
Detection Method ◽  
Corner Detection ◽  
Detection Methods ◽  
Chinese Characters ◽  
Line Segments ◽  
Running Time ◽  
Shape Feature Extraction

According to the characteristics of Chinese characters image, we propose an improved corner detection method based on FAST algorithm and Harris algorithm to improve detection rate and shorten the running time for next feature extraction in this paper. The image of Chinese characters is detected for corners using FAST algorithm Firstly. Second, computing corner response function (CRF) of Harris algorithm, false corners are removed. The corners founded lastly are the endpoints of line segments, providing the length of line segments for shape feature extraction. The proposed method is compared with several corner detection methods over a number of images. Experimental results show that the proposed method shows better performance in terms of detection rate and running time.

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