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Data-Free Knowledge Distillation (KD) allows knowledge transfer from a trained neural network (teacher) to a more compact one (student) in the absence of original training data. Existing works use a validation set to monitor the accuracy of the student over real data and report the highest performance throughout the entire process. However, validation data may not be available at distillation time either, making it infeasible to record the student snapshot that achieved the peak accuracy. Therefore, a practical data-free KD method should be robust and ideally provide monotonically increasing student accuracy during distillation. This is challenging because the student experiences knowledge degradation due to the distribution shift of the synthetic data. A straightforward approach to overcome this issue is to store and rehearse the generated samples periodically, which increases the memory footprint and creates privacy concerns. We propose to model the distribution of the previously observed synthetic samples with a generative network. In particular, we design a Variational Autoencoder (VAE) with a training objective that is customized to learn the synthetic data representations optimally. The student is rehearsed by the generative pseudo replay technique, with samples produced by the VAE. Hence knowledge degradation can be prevented without storing any samples. Experiments on image classification benchmarks show that our method optimizes the expected value of the distilled model accuracy while eliminating the large memory overhead incurred by the sample-storing methods.

Phishing refers to fraudulent social engineering techniques used to elicit sensitive information from unsuspecting victims. In this paper, our scheme is aimed at detecting phishing mails which do not contain any links but bank on the victim"s curiosity by luring them into replying with sensitive information. We exploit the common features among all such phishing emails such as nonmentioning of the victim"s name in the email, a mention of monetary incentive and a sentence inducing the recipient to reply. This textual analysis can be further combined with header analysis of the email so that a final combined evaluation on the basis of both these scores can be done. We have shown that this method is far better than the existing Phishing Email Detection techniques as this covers emails without links while the pre-existing methods were based on the presumption of link(s).

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