Attention Deficit is Ordered! Fooling Deformable Vision Transformers with Collaborative Adversarial Patches. (arXiv:2311.12914v1 [cs.CV])

The latest generation of transformer-based vision models have proven to be
superior to Convolutional Neural Network (CNN)-based models across several
vision tasks, largely attributed to their remarkable prowess in relation
modeling. Deformable vision transformers significantly reduce the quadratic
complexity of modeling attention by using sparse attention structures, enabling
them to be used in larger scale applications such as multi-view vision systems.
Recent work demonstrated adversarial attacks against transformers; we show that
these attacks do not transfer to deformable transformers due …

adversarial attention cnn complexity latest modeling network neural network patches transformers