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Transforming tracking sequence ground truth to fool Deep Convolutional Neural Networks (DCNN)

Contact: Adrien Desies

This simple experience consists essentially in creating "adversarial examples" as in [1] for whole tracking sequences.

The ground truth target patches of tracking sequences were extracted, then altered to "fool" a DCNN trained to perform image classification. That is, the error gradients of a given image for an arbitrary class were back-propagated down to the input image. By doing this iteratively, it is possible to obtain a high prediction confidence in a wrong class while altering the image subtly enough so that it is difficult for a human observer to notice. This was performed on the VOT 2013 dataset, using MatConvNet and the imagenet-vgg-f pre-trained CNN from [4].

The results can be visualized on the following youtube channel.

The procedure in a nutshell, for any image

  1. Backpropagate the error gradients down to the input image, as you would for any layer.
  2. Resize those gradients so as to have similar dimensions as your input image.
  3. Multiply those gradients by a factor which acts as a learning rate.
  4. Add the result to the input image.
  5. Iterate until the confidence in the target class is to your liking.


  • If the factor of step 3. is large, visible deformation in the image will occur.
  • On the other hand, if it is small enough, it will fool the network while the modification will be nearly invisible for the human eye

Regarding the target classes

In the following videos, when the target matched a class learned by the network, each frame was fooled in such a way that the confidence in this precise class was maximized. When no such class was available, the fooling class was decided to be something nonsensical, such as "waffle iron" and "banana".


[1] Szegedy, C.; Zaremba, W.; Sutskever, I.; Bruna, J.; Erhan, D.; Goodfellow, I. & Fergus, R. Intriguing properties of neural networks, International Conference on Learning Representations, 2014

[2] Nguyen, A.; Yosinski, J. & Clune, J. Deep Neural Networks Are Easily Fooled: High Confidence Predictions for Unrecognizable Images, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015

[3] Simonyan, K.; Vedaldi, A. & Zisserman, A. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps, CoRR?, 2013, abs/1312.6034

[4] Chatfield, K.; Simonyan, K.; Vedaldi, A. & Zisserman, A. Return of the Devil in the Details: Delving Deep into Convolutional Nets, CoRR?, 2014, abs/1405.3531

Edit | Attach | Refresh | Diffs | More | Revision r1.7 - 31 Aug 2015 - 14:44 - AdrienDesies