You See What I Want You To See: Exploring Targeted Black-Box Transferability Attack for Hash-Based Image Retrieval Systems
With the large multimedia content online, deep hashing has become a popular method for efficient image retrieval and storage. However, by inheriting the algorithmic backend from softmax classification, these techniques are vulnerable to the well-known adversarial examples as well. The massive collection of online images into the database also opens up new attack vectors. Attackers can embed adversarial images into the database and target specific categories to be retrieved by user queries. In this paper, we start from an adversarial standpoint to explore and enhance the capacity of targeted black-box transferability attack for deep hashing. We motivate this work by a series of empirical studies to see the unique challenges in image retrieval. We study the relations between adversarial subspace and black-box transferability via utilizing random noise as a proxy. Then we develop a new attack that is simultaneously adversarial and robust to noise to enhance transferability. Our experimental results demonstrate about 1.2-3x improvements of black-box transferability compared with the state-of-the-art mechanisms.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep HashingImage RetrievalRetrievalMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Exploring the Space of Black-box Attacks on Deep Neural Networks
Existing black-box attacks on deep neural networks (DNNs) so far have largely focused on transferability, where an adversarial instance generated for a locally trained model can "transfer" to attack other learning models…
A Review of Adversarial Attacks in Computer Vision
Deep neural networks have been widely used in various downstream tasks, especially those safety-critical scenario such as autonomous driving, but deep networks are often threatened by adversarial samples. Such adversaria…
Autonomous DrivingSay What I Want: Towards the Dark Side of Neural Dialogue Models
Neural dialogue models have been widely adopted in various chatbot applications because of their good performance in simulating and generalizing human conversations. However, there exists a dark side of these models -- d…
ChatbotReinforcement LearningReinforcement Learning (RL)Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks
Transfer-based adversarial attacks can evaluate model robustness in the black-box setting. Several methods have demonstrated impressive untargeted transferability, however, it is still challenging to efficiently produce …
Watch What You Pretrain For: Targeted, Transferable Adversarial Examples on Self-Supervised Speech Recognition models
A targeted adversarial attack produces audio samples that can force an Automatic Speech Recognition (ASR) system to output attacker-chosen text. To exploit ASR models in real-world, black-box settings, an adversary can l…
Adversarial AttackAutomatic Speech RecognitionAutomatic Speech Recognition (ASR)speech-recognition