If what you are working on is not important, and it is not likely to lead to important things, why are you working on it?
― Richard Hamming
16 feautres
for directed
graphs
8 feautres for
undirected
graphs
◦ For undirected graphs:
◦ For directed graphs:
We extracted 7 features
Kagan, Dima, Yuval Elovichi, and Michael Fire. "Generic anomalous vertices detection utilizing a link prediction algorithm." Social Network Analysis and Mining 8 (2018): 1-13.
Network | Is Directed | Vertices Number | Links Number | Date | Labeled |
---|---|---|---|---|---|
Academia | Yes | 200,169 | 1,389,063 | 2011 | No |
Anybeat | Yes | 12,645 | 67,053 | 2011 | No |
ArXiv HEP-PH | No | 34,546 | 421,578 | 2003 | No |
CLASS OF 1880/81 | Yes | 53 | 179 | 1881 | Yes |
DBLP | No | 1,665,850 | 13,504,952 | 2016 | No |
Google+ | Yes | 107,614 | 13,673,453 | 2012 | No |
Orkut | No | 3,072,441 | 117,185,083 | 2012 | No |
Yes | 5,384,160 | 16,011,443 | 2012 | Yes | |
No | 1,053,754 | 2,161,968 | 2012 | No | |
Yelp | No | 249,443 | 3,563,818 | 2016 | No |
AUC | TPR | FPR | Precision | |
---|---|---|---|---|
Simulation 1 (Arxiv HEP-PH) | 0.991 | 0.889 | 0.011 | 0.904 |
Simulation 2 (DBLP) | 0.997 | 0.994 | 0.064 | 0.993 |
Simulation 3 (Yelp) | 0.993 | 0.917 | 0.007 | 0.937 |
AUC | TPR | FPR | Precision | |
---|---|---|---|---|
Academia | 0.999 | 0.998 | 0.000 | 0.997 |
Anybeat | 1.000 | 0.996 | 0.001 | 0.996 |
Arxiv HEP-PH | 0.997 | 0.953 | 0.004 | 0.965 |
DBLP | 0.997 | 0.940 | 0.005 | 0.995 |
Flixster | 0.992 | 0.990 | 0.092 | 0.990 |
Google+ | 1.000 | 0.999 | 0.000 | 0.999 |
0.999 | 0.955 | 0.005 | 0.951 | |
Yelp | 0.996 | 0.941 | 0.005 | 0.958 |
Method | AUC | TPR | FPR | Precision |
---|---|---|---|---|
Ours | 0.91 | 0.89 | 0.15 | 0.964 |
Strangers | 0.71 | 0.44 | 0.006 | 1 |
Shay Lapid, Dima Kagan, and Michael Fire. “Co-Membership-based Generic Anomalous Communities Detection”. In: Neural Processing Letters (2022).
Shay Lapid, Dima Kagan, and Michael Fire. “Co-Membership-based Generic Anomalous Communities Detection”. In: Neural Processing Letters (2022).
Information Leakage
Malware Attacks
Data Breach
Fake Avatars
Phishing Attacks
Zoombombing
Credit: Such, Jose M., and Natalia Criado. "Multiparty privacy in social media." Communications of the ACM 61.8 (2018): 74-81.
89,305 Zoom related tweets
90,395 Zoom related Instagram posts
16,133 video conferncing images
26,408 images from Twitter
78,435 images from Instagram
A video conferencing detection model was trained based on ResNet-50.
Accuracy = 0.969, TPR= 0.935, FPR = 0.016
Kagan, Dima, Galit Fuhrmann Alpert, and Michael Fire. "Zooming Into Video Conferencing Privacy." IEEE Transactions on Computational Social Systems (2023).
89,305 Zoom related tweets
90,395 Zoom related Instagram posts
16,133 video conferncing images
26,408 images from Twitter
78,435 images from Instagram
A video Conferencing detection model was trained based on ResNet-50.
Accuracy = 0.969, TPR= 0.935, FPR = 0.016
Duplicates removed using dhash
Duplicates removed using the classifier last layer
15,709 unique images
Kagan, Dima, Galit Fuhrmann Alpert, and Michael Fire. "Zooming Into Video Conferencing Privacy." IEEE Transactions on Computational Social Systems (2023).
Kagan, Dima, Galit Fuhrmann Alpert, and Michael Fire. "Zooming Into Video Conferencing Privacy." IEEE Transactions on Computational Social Systems (2023).
Kagan, Dima, Galit Fuhrmann Alpert, and Michael Fire. "Zooming Into Video Conferencing Privacy." IEEE Transactions on Computational Social Systems (2023).
Kagan, Dima, Galit Fuhrmann Alpert, and Michael Fire. "Zooming Into Video Conferencing Privacy." IEEE Transactions on Computational Social Systems (2023).
Face
Detection
Face
Tracking
Feature
Extraction
Change
Detection
Group
Change
Detection
Shmuel Horowitz, Dima Kagan, Galit Fuhrmann Alpert, Michael Fire Interruptions detection in video conferences 2023
Face
Detection
Face
Tracking
Feature
Extraction
Change
Detection
Group
Change
Detection
Shmuel Horowitz, Dima Kagan, Galit Fuhrmann Alpert, Michael Fire Interruptions detection in video conferences 2023
Face
Detection
Face
Tracking
Feature
Extraction
Change
Detection
Group
Change
Detection
😃
🙂
🙂
🙂
Shmuel Horowitz, Dima Kagan, Galit Fuhrmann Alpert, Michael Fire Interruptions detection in video conferences 2023
Face
Detection
Face
Tracking
Feature
Extraction
Change
Detection
Group
Change
Detection
🙂
🙂
🙂
🙁
🙁
🙁
🙂
Shmuel Horowitz, Dima Kagan, Galit Fuhrmann Alpert, Michael Fire Interruptions detection in video conferences 2023
Face
Detection
Face
Tracking
Feature
Extraction
Change
Detection
Group
Change
Detection
Shmuel Horowitz, Dima Kagan, Galit Fuhrmann Alpert, Michael Fire Interruptions detection in video conferences 2023
Face
Detection
Face
Tracking
Feature
Extraction
Change
Detection
Group
Change
Detection
Feature Extaction | Change Detection | Recall | Precision | TNR | FPR |
---|---|---|---|---|---|
Embedding 128-dim | Arima Statistical Profiling |
50.1% 42.9% |
83.2% 75.0% |
98.1% 97.8% |
0.9% 1.4% |
Expressions 7-dim | Arima Statistical Profiling |
71.6% 72.5% |
90.5% 92.3% |
99.2% 99.1% |
0.5% 0.8% |
Deep-face expressions | Majority | 67.1% | 84.8% | 97.7% | 2.6% |
Method | Results |
---|
Shmuel Horowitz, Dima Kagan, Galit Fuhrmann Alpert, Michael Fire Interruptions detection in video conferences 2023
Shmuel Horowitz, Dima Kagan, Galit Fuhrmann Alpert, Michael Fire Interruptions detection in video conferences 2023
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