1. Spam Image Identification Using an Artificial Neural Network
2008 MIT Spam Conference
Spam Image Identification Using an Artificial Neural Network
Jason R. Bowling, Priscilla Hope and Kathy J. Liszka
The University of Akron

8. We know it’s bad… 2005 – roughly 1% of all emails
mid 2006 – rose to 21%
J. Swartz, “Picture this: A sneakier kind of spam,”
USA Today, Jul. 23, 2006.

9. The University of Akron
December 2007
28,000,000 messages
24,000,000 identified as spam and dropped

10. Inspiration

11. FANN Fast Artificial Neural Network Library open source
adaptive, learn by example (given good input)
input
hidden
output

12. Image Preparation open source
converts from virtually any format to another
tradeoffs

13. image2fann.cpp input images training data 150 × 150 pixel
8-bit grayscale
jpg images

14. number of
input nodes
number of
output nodes
number of images
(input sets) … 1 … -1
spam
ham

15. 22,500
input
nodes
two layers
of hidden nodes
1 output
node

16. Training the Network
A fully connected back propagation neural network.
Supervised learning paradigm.

17. Activation Function
Takes the inputs to a node, uses a weight for each input and determines the weight of the output from the node.

18. Steepness
1.0
0.5
0.0

19. Widrow and Nguyen’s algorithm
An even distribution of weights across each input node’s active region.
Used at initialization.

20. Epoch
One cycle where the weights are adjusted to match the output in the training file.
I’m spam!
Too many epochs can cause the ANN to fail.
I’m ham!

21. Learning Rate Train to a desired error.
Step down the training rate at preset intervals to avoid oscillation.

22. Training 22604 nodes in network Max epochs 200. Desired error: 0.4
Epochs Current error: Bit fail 56.
Learning rate is:
Max epochs Desired error:
Epochs Current error: Bit fail 56.
Epochs Current error: Bit fail 56.
Epochs Current error: Bit fail 65.
Epochs Current error: Bit fail 48.

23. image2fann.cpp
train.c
test.c
ham
spam
input
images
training
data
FANN

24. 572 Trained Images 75 hidden nodes

25. 572 Trained Images 50 hidden nodes

26. Corpus

28. Scaling to number < 1
(divide by 1000)
grayscale intensity
training
data
limited to 0 – 0.25

29. Current Work complete corpus multipart images separate ANNs
hidden nodes
color
image size

34. Priscilla Hope

35. Thank you!