Traits & Transferability of Adversarial Examples against Instance Segmentation & Object Detection
This work addresses the problem of adversarial robustness for researchers and practitioners deploying complex vision models, showing that current attacks are incremental and limited in real-world applicability.
The paper tackles the threat of adversarial examples by evaluating their characteristics and transferability to instance segmentation and object detection models, finding that these attacks are largely ineffective due to their inability to withstand input transformations like scaling or lighting changes, with only a small threshold retaining adversarial properties.
Despite the recent advancements in deploying neural networks for image classification, it has been found that adversarial examples are able to fool these models leading them to misclassify the images. Since these models are now being widely deployed, we provide an insight on the threat of these adversarial examples by evaluating their characteristics and transferability to more complex models that utilize Image Classification as a subtask. We demonstrate the ineffectiveness of adversarial examples when applied to Instance Segmentation & Object Detection models. We show that this ineffectiveness arises from the inability of adversarial examples to withstand transformations such as scaling or a change in lighting conditions. Moreover, we show that there exists a small threshold below which the adversarial property is retained while applying these input transformations. Additionally, these attacks demonstrate weak cross-network transferability across neural network architectures, e.g. VGG16 and ResNet50, however, the attack may fool both the networks if passed sequentially through networks during its formation. The lack of scalability and transferability challenges the question of how adversarial images would be effective in the real world.