TagSense: Leveraging Smartphones For Automatic Image Tagging

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TagSense is a smartphone-based system that leverages built-in sensors to automatically tag images with descriptions of when, where, who, and what is happening in the picture. It uses sensors like GPS, light sensors, accelerometers, microphones, and cameras to determine the date, location, activities, and people present. TagSense then organizes these tags into a "When-Where-Who-What" format to automatically describe pictures. While promising, TagSense has limitations like a limited vocabulary of tags, inability to generate full captions, and complex methods for identifying people.

Engineering

TagSense: Leveraging Smartphones
For Automatic Image Tagging
PRESENTED BY: VIJAY MALI
GUIDED BY:
PROF. P. D. GANJEWAR

Overview::-
 Motivation
 Introduction
 Scope Of TagSense
 System Architecture
 Design And Methodology
 Performance Evaluation
 Flowchart
 Limitations of TagSense
 Conclusion

Motivation::-
 Human Tagging: Time Consuming And Boring
 Advantages over facial recognition
 Could save humans from the task of manually tagging photos
 Could be paired with facial recognition to make a robust
solution
 Today’s smartphones have powerful built-in sensors People
always carry their phones.

Introduction::-
 It is a mobile phone–based collaborative system that
senses the people, activity and context in a picture and
merges them to create tags .
 These tags are organized into a “When-Where-Who-What”
format, ultimately creating an automatic description for
picture

Scope Of TagSense::-
 Picture 1: November 21, NasherMuseum, indoor, Romit, Sushma, Naveen,
Souvik, Justin, Xuan, standing, talking.
 Picture 2: December 4, hdsonhall, outdoor, Xuan, standing, snowing.

Design And Methodology::-
 When is the picture taken?
Time and date
Time of day (afternoon, evening, night …)
Weather
 Where is the picture taken?
GPS ‐ Reverse lookup
Light sensor (indoor/outdoor)
 What are they doing?
Accelerometer
Acoustic
Vocabulary of activities recognized by phone sensors
(standing, walking, talking …)

Who Is In The Picture?
Possible Opportunities::-
1. Accelerometer based motion signatures
2. Complementary compass directions
3. Correlating visual and acceleration

1. Accelerometer Based Motion
Signature
 When subject pose for a picture, phones
exhibit a motion signature .
 TagSense find such a signature around the
time of picture click.

2. Complementary Compass
Direction
 The posing signature will be absent in some cases.
 In such cases the direction of their compasses will be
roughly complementary to the camera’s facing
direction.

3. Correlating Visual And
Acceleration
(a) First snap shot (b) Second snap shot
(c)Optical flow of each pixel

3. Correlating Visual And
Acceleration
 Taking several snapshots after shutter click
 Motion vector can be obtained by optical flow from adjacent
snapshots
 Correlate with accelerometer readings to find who
(d) Motion vectors for two
objects
(c)Optical flow of each pixel

Flowchart And Algorithms::-
 Flowchart:

Limitations of Tagsense::-
 TagSense vocabulary of tags is quite limited.
 It does not generate effective sentenced captions.
 TagSense methods for tagging people are complex.
 TagSense cannot tag kids as they are not likely to
have phones.

Conclusion::-
 Mobile phones are becoming inseparable from
humans and are replacing traditional cameras.
 TagSense leverages this trend to automatically tag
pictures with people and their activities
 TagSense has somewhat lower precision and
comparable fall-out but significantly higher recall
than iPhoto/Picasa.

References::-
 Chuan Qin, Xuan Bao, Romit Roy Choudhury and
Nelakuditi,” TagSense: leveraging smartphones for
automatic image tagging”, IEEE Transcation on mobile
computing, vol 13, No 1, January 2014.
 (Part1) TagSense A Smartphone‐based Approach to
Automatic Image Tagging – YouTube, Chuan Qin
 (Part2) TagSense A Smartphone‐based Approach to
Automatic Image Tagging – YouTube, Chuan Qin

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#Abstract: - Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices. - Gain actionable insights into AWS IAM policies and roles, using hands on approach. #Prerequisites: - Basic understanding of AWS services and architecture - Familiarity with cloud security concepts - Experience using the AWS Management Console or AWS CLI. - For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/) # Scenario Covered: - Basics of IAM in AWS - Implementing IAM Policies with Least Privilege to Manage S3 Bucket - Objective: Create an S3 bucket with least privilege IAM policy and validate access. - Steps: - Create S3 bucket. - Attach least privilege policy to IAM user. - Validate access. - Exploiting IAM PassRole Misconfiguration -Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources. - Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access. - Steps: - Allow user to pass IAM role to EC2. - Exploit misconfiguration for unauthorized access. - Access sensitive resources. - Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role - An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role. - Objective: Show how overly permissive IAM roles can lead to privilege escalation. - Steps: - Create role with administrative privileges. - Allow user to assume the role. - Perform administrative actions. - Differentiation between PassRole vs AssumeRole Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)

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Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.

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TagSense: Leveraging Smartphones For Automatic Image Tagging

1. TagSense: Leveraging Smartphones For Automatic Image Tagging PRESENTED BY: VIJAY MALI GUIDED BY: PROF. P. D. GANJEWAR

2. Overview::-  Motivation  Introduction  Scope Of TagSense  System Architecture  Design And Methodology  Performance Evaluation  Flowchart  Limitations of TagSense  Conclusion

3. Motivation::-  Human Tagging: Time Consuming And Boring  Advantages over facial recognition  Could save humans from the task of manually tagging photos  Could be paired with facial recognition to make a robust solution  Today’s smartphones have powerful built-in sensors People always carry their phones.

4. Introduction::-  It is a mobile phone–based collaborative system that senses the people, activity and context in a picture and merges them to create tags .  These tags are organized into a “When-Where-Who-What” format, ultimately creating an automatic description for picture

5. Scope Of TagSense::-  Picture 1: November 21, NasherMuseum, indoor, Romit, Sushma, Naveen, Souvik, Justin, Xuan, standing, talking.  Picture 2: December 4, hdsonhall, outdoor, Xuan, standing, snowing.

6. System Architecture::-

7. Design And Methodology::-  When is the picture taken? Time and date Time of day (afternoon, evening, night …) Weather  Where is the picture taken? GPS ‐ Reverse lookup Light sensor (indoor/outdoor)  What are they doing? Accelerometer Acoustic Vocabulary of activities recognized by phone sensors (standing, walking, talking …)

8. Design And Methodology::-

9. Who Is In The Picture? Possible Opportunities::- 1. Accelerometer based motion signatures 2. Complementary compass directions 3. Correlating visual and acceleration

10. 1. Accelerometer Based Motion Signature  When subject pose for a picture, phones exhibit a motion signature .  TagSense find such a signature around the time of picture click.

11. 2. Complementary Compass Direction  The posing signature will be absent in some cases.  In such cases the direction of their compasses will be roughly complementary to the camera’s facing direction.

12. 3. Correlating Visual And Acceleration (a) First snap shot (b) Second snap shot (c)Optical flow of each pixel

13. 3. Correlating Visual And Acceleration  Taking several snapshots after shutter click  Motion vector can be obtained by optical flow from adjacent snapshots  Correlate with accelerometer readings to find who (d) Motion vectors for two objects (c)Optical flow of each pixel

14. Flowchart And Algorithms::-  Flowchart:

15. Comparison With iPhoto And Picasa

16. Limitations of Tagsense::-  TagSense vocabulary of tags is quite limited.  It does not generate effective sentenced captions.  TagSense methods for tagging people are complex.  TagSense cannot tag kids as they are not likely to have phones.

17. Conclusion::-  Mobile phones are becoming inseparable from humans and are replacing traditional cameras.  TagSense leverages this trend to automatically tag pictures with people and their activities  TagSense has somewhat lower precision and comparable fall-out but significantly higher recall than iPhoto/Picasa.

18. References::-  Chuan Qin, Xuan Bao, Romit Roy Choudhury and Nelakuditi,” TagSense: leveraging smartphones for automatic image tagging”, IEEE Transcation on mobile computing, vol 13, No 1, January 2014.  (Part1) TagSense A Smartphone‐based Approach to Automatic Image Tagging – YouTube, Chuan Qin  (Part2) TagSense A Smartphone‐based Approach to Automatic Image Tagging – YouTube, Chuan Qin

19. Thank You…!! ANY QUESTIONS ?

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