K. So Yeon, B. Yenewondim, and S. Kyung-Ah, "Investigating the Effectiveness of E-mail Spam Image Data for Phone Spam Image Detection Using Scale Invariant Feature Transform Image Descriptor.", Information Science and Applications, LNEE 339, 2015, in press.

1. Investigating the Effectiveness of E-mail Spam Image
Data for Phone Spam Image Detection Using Scale
Invariant Feature Transform Image Descriptor
So Yeon Kim, Yenewondim Biadgie, Kyung-Ah Sohn
Department of Information and Computer Engineering, Ajou University

2. Motivation

3. Image spams in mobile phone
Image spams are rapidly increasing
instead of text spam
Image spam detection
is also needed in mobile phone !

4. Challeges

5. Data (mobile phone)
 66 spam images, 405 non-spam images
 training data – 377 images (80%)
 test data - 94 images (20%)
Too small dataset
Hard to train model

6. Data (E-mail)
1. Image Spam hunter (2008)
 929 spam, 810 non-spam
2. Dredze et al (2007)
 3,299 spam, 2,021 non-spam
3. TREC Spam track (2005)
 60,339 spam, 165,954 non-spam
 TREC 06, TREC 07 are also available.
Would be better to
use those huge dataset

7. How to use e-mail image data?
They are much different from
phone spam images

8. How to use e-mail image data?
But, some images
look similar to phone spam images

9. How to use e-mail image data?
Similarity measure

10. Methods

11. Data acquisition
Smart phone
spam images
E-mail
spam images
𝑋2 distance
Similarity matrix
Phoneimage
Email image
66 spam images
929 spam images
…
RGB histogram feature vector
…

12. Data acquisition
K-means
Clustering
Most similar
email images
Phone + Email
Spam Image
Dataset
Phone
images
Total 419 spam images
353 images

13. Data acquisition
Spam Non-spam
Phone Spam Dataset 66 405
Image Spam Hunter (08) 353 -
Total 419 405

14. Feature Extraction
Input image PHOW feature extraction K-means
clustering
500 visual word dictionary construction• Dense grayscale SIFT
• Much faster than SIFT
Visual dictionaryVisual
word
VLFeat library is used for implementation

15. Feature Extraction
Spatial histogram
KD-tree vector
quantization
Histogram(bag) of
visual words
VLFeat library is used for implementation

16. Image classification
Image descriptor
SVM classification
spam
non-sam
VLFeat library is used for implementation

17. Evaluation
Spam Non-spam
Phone Spam Dataset 66 405
Image Spam Hunter (08) 353 -
Total 419 405
Training set
Test set
E-mail Phone
5-fold
cross validation

18. Results
k-means random (50%)
Accuracy 96.39% 95.12%
Sensitivity 94.07% 89.45%
Specificity 96.79% 96.05%
F-measure 87.94% 83.80%

19. Results
False positives False negatives

20. Conclusion
 We tried to solve the problem of data acquisition for phone spam
image classification using e-mail image dataset.
 Using email spam image data gained by similarity measure
is quite effective for phone spam image classification.
 If email data size becomes larger, it has many kinds of feature
group.
→ a more precise clustering algorithm could be useful
for the future.

21. Thank you !