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Found 11 papers, 4 papers with code
Boost Adversarial Transferability by Uniform Scale and Mix Mask Method
To address these challenges, we propose a framework called Uniform Scale and Mix Mask Method (US-MM) for adversarial example generation.
Real-centric Consistency Learning for Deepfake Detection
Essentially, the target of deepfake detection problem is to represent natural faces and fake faces at the representation space discriminatively, and it reminds us whether we could optimize the feature extraction procedure at the representation space through constraining intra-class consistence and inter-class inconsistence to bring the intra-class representations close and push the inter-class representations apart?
Understanding CNNs from excitations
To organize these excitations into final saliency maps, we introduce a double-chain backpropagation procedure.
Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?
Our main findings are: (i) predictive uncertainty indeed helps achieve reliable malware detection in the presence of dataset shift, but cannot cope with adversarial evasion attacks; (ii) approximate Bayesian methods are promising to calibrate and generalize malware detectors to deal with dataset shift, but cannot cope with adversarial evasion attacks; (iii) adversarial evasion attacks can render calibration methods useless, and it is an open problem to quantify the uncertainty associated with the predicted labels of adversarial examples (i. e., it is not effective to use predictive uncertainty to detect adversarial examples).
Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection
This motivates us to investigate which kind of robustness the ensemble defense or effectiveness the ensemble attack can achieve, particularly when they combat with each other.
Arms Race in Adversarial Malware Detection: A Survey
In this paper, we survey and systematize the field of Adversarial Malware Detection (AMD) through the lens of a unified conceptual framework of assumptions, attacks, defenses, and security properties.
A Framework for Enhancing Deep Neural Networks Against Adversarial Malware
By conducting experiments with the Drebin Android malware dataset, we show that the framework can achieve a 98. 49\% accuracy (on average) against grey-box attacks, where the attacker knows some information about the defense and the defender knows some information about the attack, and an 89. 14% accuracy (on average) against the more capable white-box attacks, where the attacker knows everything about the defense and the defender knows some information about the attack.
An Overview of Two Age Synthesis and Estimation Techniques
Age estimation is defined to label a facial image automatically with the age group (year range) or the exact age (year) of the person's face.
Time-Aware Gated Recurrent Unit Networks for Road Surface Friction Prediction Using Historical Data
The findings can help improve the prediction accuracy and efficiency of forecasting road surface friction using historical data sets with missing values, therefore mitigating the impact of wet or icy road conditions on traffic safety.
Enhancing Robustness of Deep Neural Networks Against Adversarial Malware Samples: Principles, Framework, and AICS'2019 Challenge
However, machine learning is known to be vulnerable to adversarial evasion attacks that manipulate a small number of features to make classifiers wrongly recognize a malware sample as a benign one.
Cryptography and Security 68-06
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples
Adversarial machine learning in the context of image processing and related applications has received a large amount of attention.
