Determining Sequence of Image Processing Technique (IPT) to Detect Adversarial Attacks

• URL: http://arxiv.org/abs/2007.00337v2
• Date: Tue, 7 Jul 2020 09:26:57 GMT
• Title: Determining Sequence of Image Processing Technique (IPT) to Detect Adversarial Attacks
• Authors: Kishor Datta Gupta, Zahid Akhtar, Dipankar Dasgupta
• Abstract summary: We propose an evolutionary approach to automatically determine Image Processing Techniques Sequence (IPTS) for detecting malicious inputs. A detection framework based on a genetic algorithm (GA) is developed to find the optimal IPTS. A set of IPTS selected dynamically in testing time which works as a filter for the adversarial attack.
• Score: 4.431353523758957
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Developing secure machine learning models from adversarial examples is challenging as various methods are continually being developed to generate adversarial attacks. In this work, we propose an evolutionary approach to automatically determine Image Processing Techniques Sequence (IPTS) for detecting malicious inputs. Accordingly, we first used a diverse set of attack methods including adaptive attack methods (on our defense) to generate adversarial samples from the clean dataset. A detection framework based on a genetic algorithm (GA) is developed to find the optimal IPTS, where the optimality is estimated by different fitness measures such as Euclidean distance, entropy loss, average histogram, local binary pattern and loss functions. The "image difference" between the original and processed images is used to extract the features, which are then fed to a classification scheme in order to determine whether the input sample is adversarial or clean. This paper described our methodology and performed experiments using multiple data-sets tested with several adversarial attacks. For each attack-type and dataset, it generates unique IPTS. A set of IPTS selected dynamically in testing time which works as a filter for the adversarial attack. Our empirical experiments exhibited promising results indicating the approach can efficiently be used as processing for any AI model.

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