is concerned with the design of ML algorithms that can resist security challenges. Adversarial machine learning Adversarial Machine Learning states that there are that ML models can suffer. four types of attacks Extraction attacks In a model , an adversary steals a copy of a remotely deployed machine learning model, given oracle prediction access. extraction attack It is produced by making requests to the target model with inputs to extract as much information as possible and with the set of inputs and outputs train a model called substitute model. , the attacker needs a huge compute capacity to re-training the new model with accuracy and fidelity, and substitute model is equivalent to training a model from the ground up. Extract model is hard Defenses Limit the output information when the model classifies a given input. Differential Privacy. Use . ensembles Proxy between end-user and model like . PRADA Limit the number of requests. Inference attacks Inference attacks aim to reverse the information flow of a machine learning model. They allow an adversary to have knowledge of the model that was not explicitly intended to be shared. Inference attacks pose severe privacy and security threats to individuals and systems. They are successful because private data are statistically correlated with public data, and ML classifiers can capture such statistical correlations. Includes three types of attacks: Membership Inference Attack (MIA). Property Inference Attack (PIA). Recovery training data. Defenses Use advanced cryptography. Differential cryptography. Homomorphic cryptography. Secure Multi-party Computation. Techniques such as Dropout. Model compression. Poisoning attacks This technique involves an in the training dataset to compromise a target machine learning model during training. attacker inserting corrupt data Some data aim to trigger a specific behavior in a computer vision system when it faces a specific pattern of pixels at inference time. Other data poisoning techniques aim to reduce the accuracy of a machine learning model on one or more output classes. poisoning techniques This attack is difficult to detect when performed on training data since the attack can propagate between different models using the same data. The adversary seeks to destroy the availability of the model by modifying the decision boundary and, as a result, producing incorrect predictions. Finally, the attacker could create a .  The model behaves correctly (returning the desired predictions) in most cases, except for certain inputs specially created by the adversary that produce undesired results. The the results of the predictions and launch future attacks. backdoor in a model adversary can manipulate Defenses Protect the integrity of training data. Protect the algorithms, use robust methods to train models. Evasion attacks An (in the form of noise) into the input of a machine learning model to make it (example adversary). adversary inserts a small perturbation classify incorrectly They are similar to poisoning attacks, but their main difference is that evasion attacks try to exploit weaknesses of the model in the inference phase, not in the training. The attacker’s knowledge of the target system is important. The more they know about your model and how it’s built — the easier it is for them to mount an attack on it. An evasion attack happens when the network is fed an “adversarial example” — a carefully perturbed input that looks and feels exactly the same as its — but that completely . untampered copy to a human throws off the classifier Defenses Training with adversarial examples which robust the model. Transform the input to the model (Input sanitization). Gradient regularization. Tools Adversarial Robustness Toolbox (ART) is a Python library for Machine Learning Security. ART provides tools that enable developers and researchers to defend and evaluate Machine Learning models and applications against the adversarial threats of , , , and . Adversarial Robustness Toolbox (ART) Evasion Poisoning Extraction Inference ART supports all popular machine learning frameworks: TensorFlow Keras PyTorch scikit-learn All data types: Images Tables Audio Video And machine learning tasks: Classification Object detection Speech recognition pip installation pip install adversarial-robustness-toolbox Attack example from art.attacks.evasion import FastGradientMethod
attack_fgm = FastGradientMethod(estimator = classifier, eps = 0.2)
x_test_fgm = attack_fgm.generate(x=x_test)
predictions_test = classifier.predict(x_test_fgm) Defense example from art.defences.trainer import AdversarialTrainer
model.compile(loss=keras.losses.categorical_crossentropy, optimizer=tf.keras.optimizers.Adam(lr=0.01), metrics=["accuracy"])
defence = AdversarialTrainer(classifier=classifier, attacks=attack_fgm, ratio=0.6)
(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()
defence.fit(x=x_train, y=y_train, nb_epochs=3) Counterfit is a command-line tool and generic automation layer for assessing the security of machine learning systems. Counterfit Developed for security audits on ML models. Implements black box evasion algorithms and based on ART and TextAttack. Command list ---------------------------------------------------
Microsoft
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/ /__/ /_/ / /_/ / / / / /_/  __/ /  / __/ / /
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                                        #ATML

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list targets

list frameworks

load <framework> 

list attacks

interact <target>

predict -i <ind>

use <attack>

run

scan Final words and by "If you use machine learning, there is the risk for exposure, even though the threat does not currently exist in your space." "The gap between machine learning and security is definitely there." Hyrum Anderson, Microsoft ## References Towards Security Threats of DL Systems Adversarial Matrix Mitre Poisoning attacks Evasion attacks Thanks Special thanks to , co-writer of this article. @jiep This article was first published . here

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Adversarial Machine Learning: A Beginner’s Guide to Adversarial Attacks and Defenses

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