Pairwise Confusion for Fine-Grained Visual Classification

Fine-Grained Visual Classification (FGVC) datasets contain small sample sizes, along with significant intra-class variation and inter-class similarity. While prior work has addressed intra-class variation using localization and segmentation techniques, inter-class similarity may also affect feature learning and reduce classification performance. In this work, we address this problem using a novel optimization procedure for the end-to-end neural network training on FGVC tasks. Our procedure, called Pairwise Confusion (PC) reduces overfitting by intentionally {introducing confusion} in the activations. With PC regularization, we obtain state-of-the-art performance on six of the most widely-used FGVC datasets and demonstrate improved localization ability. {PC} is easy to implement, does not need excessive hyperparameter tuning during training, and does not add significant overhead during test time.Comment: Camera-Ready version for ECCV 201

Comparison of Secret Splitting, Secret Sharing and Recursive Threshold Visual Cryptography for Security of Handwritten Images

The secret sharing is a method to protect confidentiality and integrity of the secret messages by distributing the message shares into several recipients. The secret message could not be revealed unless the recipients exchange and collect shares to reconstruct the actual message. Even though the attacker obtain shares shadow during the share exchange, it would be impossible for the attacker to understand the correct share. There are few algorithms have been developed for secret sharing, e.g. secret splitting, Asmuth-Bloom secret sharing protocol, visual cryptography, etc. There is an unanswered question in this research about which method provides best level of security and efficiency in securing message. In this paper, we evaluate the performance of three methods, i.e. secret splitting, secret sharing, and recursive threshold visual cryptography for handwritten image security in terms of execution time and mean squared error (MSE) simulation. Simulation results show the secret splitting algorithm produces the shortest time of execution. On the other hand, the MSE simulation result that the three methods can reconstruct the original image very well

Inline Detection of Domain Generation Algorithms with Context-Sensitive Word Embeddings

Domain generation algorithms (DGAs) are frequently employed by malware to generate domains used for connecting to command-and-control (C2) servers. Recent work in DGA detection leveraged deep learning architectures like convolutional neural networks (CNNs) and character-level long short-term memory networks (LSTMs) to classify domains. However, these classifiers perform poorly with wordlist-based DGA families, which generate domains by pseudorandomly concatenating dictionary words. We propose a novel approach that combines context-sensitive word embeddings with a simple fully-connected classifier to perform classification of domains based on word-level information. The word embeddings were pre-trained on a large unrelated corpus and left frozen during the training on domain data. The resulting small number of trainable parameters enabled extremely short training durations, while the transfer of language knowledge stored in the representations allowed for high-performing models with small training datasets. We show that this architecture reliably outperformed existing techniques on wordlist-based DGA families with just 30 DGA training examples and achieved state-of-the-art performance with around 100 DGA training examples, all while requiring an order of magnitude less time to train compared to current techniques. Of special note is the technique's performance on the matsnu DGA: the classifier attained a 89.5% detection rate with a 1:1,000 false positive rate (FPR) after training on only 30 examples of the DGA domains, and a 91.2% detection rate with a 1:10,000 FPR after 90 examples. Considering that some of these DGAs have wordlists of several hundred words, our results demonstrate that this technique does not rely on the classifier learning the DGA wordlists. Instead, the classifier is able to learn the semantic signatures of the wordlist-based DGA families.Comment: 6 pages, 5 figures, 2 table