Padding

Abstract: A method for padding, filtering, denoising, image enhancing and increased time-frequency acquisition is described for digitized data of a data set is described where unknown data is estimated using real data by adding unknown data points in a manner that the padding routine can estimate the interior data set including known and unknown data to a given accuracy on the known data points. The method also provides filtering using non-interpolating, well-tempered distributed approximating functional (NIDAF)-low-band-pass filters. The method also provides for symmetric and/or anti-symmetric extension of the data set so that the data set may be better refined and can be filtered by Fourier and other type of low frequency or harmonic filters.

Abstract: In many standards, e.g. SSL/TLS, IPSEC, WTLS, messages are first pre-formatted, then encrypted in CBC mode with a block cipher. Decryption needs to check if the format is valid. Validity of the format is easily leaked from communication protocols in a chosen ciphertext attack since the receiver usually sends an acknowledgment or an error message. This is a side channel.In this paper we show various ways to perform an efficient side channel attack. We discuss potential applications, extensions to other padding schemes and various ways to fix the problem.

Abstract: Anonymous communication has become a hot research topic in order to meet the increasing demand for web privacy protection However, there are few such systems which can provide high level anonymity for web browsing The reason is the current dominant dummy packet padding method for anonymization against traffic analysis attacks This method inherits huge delay and bandwidth waste, which inhibits its use for web browsing In this paper, we propose a predicted packet padding strategy to replace the dummy packet padding method for anonymous web browsing systems The proposed strategy mitigates delay and bandwidth waste significantly on average We formulated the traffic analysis attack and defense problem, and defined a metric, cost coefficient of anonymization (CCA), to measure the performance of anonymization We thoroughly analyzed the problem with the characteristics of web browsing and concluded that the proposed strategy is better than the current dummy packet padding strategy in theory We have conducted extensive experiments on two real world data sets, and the results confirmed the advantage of the proposed method

Abstract: Automatic saliency prediction in 360° videos is critical for viewpoint guidance applications (e.g., Facebook 360 Guide). We propose a spatial-temporal network which is (1) weakly-supervised trained and (2) tailor-made for 360° viewing sphere. Note that most existing methods are less scalable since they rely on annotated saliency map for training. Most importantly, they convert 360° sphere to 2D images (e.g., a single equirectangular image or multiple separate Normal Field-of-View (NFoV) images) which introduces distortion and image boundaries. In contrast, we propose a simple and effective Cube Padding (CP) technique as follows. Firstly, we render the 360° view on six faces of a cube using perspective projection. Thus, it introduces very little distortion. Then, we concatenate all six faces while utilizing the connectivity between faces on the cube for image padding (i.e., Cube Padding) in convolution, pooling, convolutional LSTM layers. In this way, CP introduces no image boundary while being applicable to almost all Convolutional Neural Network (CNN) structures. To evaluate our method, we propose Wild-360, a new 360° video saliency dataset, containing challenging videos with saliency heatmap annotations. In experiments, our method outperforms baseline methods in both speed and quality.