Sumohana S Channappayya

Suresh Kumar Amalapuram, Bikraj Shresta, Siva Ram murthy Chebiyam et al.

Machine learning based malware detectors become obsolete over time due to concept drift in benign and malware applications. Recent methods rely on fully labeled data and use hierarchical contrastive loss (HCL) with active learning to improve robustness against drift by exploiting semantic structure in malware representations. However, obtaining labeled data in the security domain is difficult. Under partially labeled settings, HCL suffers significant performance degradation in detecting unseen malware, especially on datasets such as BODMAS where strong semantic structure may not exist. In this paper, we propose SEED, a semantic-structure-agnostic method for malware detection under limited supervision. SEED combines a tailored binary cross-entropy objective with semi-supervised continual learning and active learning. For partially labeled seen tasks, unlabeled samples are projected into a representation space constructed from previously seen data using singular value decomposition, and paired with suitable labeled samples to encourage representation consistency. For unseen tasks with fully unlabeled data, uncertainty is quantified using cosine distance in representation space, and the most uncertain samples are selected for analyst labeling. We evaluate SEED on both Windows and Android malware datasets. Using only 20% labeled data on seen tasks, SEED achieves average AUT improvements of 40% on BODMAS and 14% on AndroZoo for unseen malware detection compared to HCL* (the semi-supervised adaptation of HCL), while remaining competitive on APIGraph. Finally, we introduce a delayed buffer update strategy to reduce label noise propagation during replay and improve learning stability.

Sria Biswas, Balasubramanyam Appina, Priyanka Kokil et al.

We present two major contributions in this work: 1) we create a full HD resolution stereoscopic (S3D) video dataset comprised of 12 reference and 360 distorted videos. The test stimuli are produced by simulating the five levels of fog and haze ambiances on the pristine left and right video sequences. We perform subjective analysis on the created video dataset with 24 viewers and compute Difference Mean Opinion Scores (DMOS) as quality representative of the dataset, 2) an Opinion Unaware (OU) and Distortion Unaware (DU) video quality assessment model is developed for S3D videos. We construct cyclopean frames from the individual views of an S3D video and partition them into nonoverlapping blocks. We analyze the Natural Scene Statistics (NSS) of all patches of pristine and test videos, and empirically model the NSS features with Univariate Generalized Gaussian Distribution (UGGD). We compute UGGD model parameters (α, \b{eta}) at multiple spatial scales and multiple orientations of spherical steerable pyramid decomposition and show that the UGGD parameters are distortion discriminable. Further, we perform Multivariate Gaussian (MVG) modeling on the pristine and distorted video feature sets and compute the corresponding mean vectors and covariance matrices of MVG fits. We compute the Bhattacharyya distance measure between mean vectors and covariance matrices to estimate the perceptual deviation of a test video from pristine video set. Finally, we pool both distance measures to estimate the overall quality score of an S3D video. The performance of the proposed objective algorithm is verified on the popular S3D video datasets such as IRCCYN, LFOVIAS3DPh1, LFOVIAS3DPh2 and the proposed VAD stereo dataset. The algorithm delivers consistent performance across all datasets and shows competitive performance against off-the-shelf 2D and 3D image and video quality assessment algorithms.