arXiv subjects

Shahzad Ahmad

Publications and source records attributed to Shahzad Ahmad.

9 recordsLinked to original sources

arXiv2026

Positive Pair Geometry Matters: Optimal Transport for Contrastive Learning of Visual Representations

Contrastive self-supervised learning has achieved strong performance by learning representations from multiple augmented views of the same image. However, most existing methods construct positive pairs using independently sampled stochastic augmentations, which may alter semantic content and ignore the intrinsic geometry of the data distribution. In this work, we propose OTCLR, an optimal transport-aware framework for contrastive learning representations that generates geometry-consistent positive samples. Instead of directly contrasting two randomly augmented views, we construct intermediate views between the original image and its augmented variants through entropic optimal-transport displacement interpolation. These transport-interpolated samples serve as positive views that better preserve image structure while explicitly modeling spatial distributional geometry. To further promote smooth representation learning, we evaluate auxiliary Sinkhorn regularization terms that encourage transport-interpolated views to remain consistent with their endpoint images. The proposed method can be incorporated into standard contrastive learning pipelines without modifying the encoder architecture. Experiments on multiple benchmark datasets show that our approach improves representation quality and transfer learning performance compared with conventional augmentation-based contrastive learning baselines.

cs.CV↗

arXiv2026

SpEmoC: A Balanced Speaker-Segment Multimodal Emotion Benchmark

Understanding human emotions in spoken conversations is a key challenge in affective computing, with applications in empathetic AI, human computer interaction, and mental health monitoring. However, existing datasets vary in scale, emotion distribution, modality alignment, and data partitioning strategies, which can influence reliable cross-dataset generalization and minority-emotion modeling. We introduce SpEmoC a Speaking segment Emotion for Conversations comprising 306,544 raw clips from 3,100 English language movies and TV series. From these, 30,000 high quality, class balanced clips are curated, featuring synchronized visual, audio, and textual modalities annotated for seven emotions through a hybrid pipeline that integrates pretrained models with human validation. SpEmoC uses strict movie- and series-level splits to prevent content overlap between split sets, allowing more reliable evaluation of model generalization. The dataset also maintains a near-balanced distribution across seven emotions, including minority classes such as Fear and Disgust, which supports more balanced learning across categories. Extensive experiments, including in-domain benchmarking, cross-dataset transfer, low-data training, class-imbalance analysis, and modality transfer show that balanced data and careful splitting lead to more stable performance across emotions when models are evaluated on other datasets. These results highlight the importance of dataset design for robust and transferable multimodal emotion recognition.

cs.CV↗

arXiv2026

Plausible Deniability in Fully Homomorphic Computation

We introduce \emph{Plausible Deniability in Fully Homomorphic Computation} (PD-FHC), a framework enabling users to outsource Boolean computations to an untrusted cloud while maintaining both computational privacy against honest-but-curious providers and plausible deniability against coercive adversaries. We define the notion of a \emph{Deniable Computation Medium} (DCM) and a \emph{Deniable Computation Scheme} (DCS) as medium-independent abstractions, then instantiate them using RGB images with Fredkin-gate circuits. One real circuit and several decoys share a single fixed Fredkin-gate wiring. Embedded control bits decide what each gate computes at each pixel, so the same wiring evaluates the real function at the real positions and decoy functions elsewhere. The cloud applies this one wiring to every pixel identically, processing all circuits in a single pass. Under coercion, the user reveals a decoy with verifiable results while the real circuit stays hidden. We formalize multi-round coercion games with existence and circuit-discovery advantages. For the image instantiation, we prove \emph{information-theoretic position privacy} under a \emph{matched-marginal condition}: when the real, decoy, and fill bits are drawn from a common per-position law and placed at random, the embedded LSB plane is exchangeable, so an honest-but-curious provider gains no advantage over guessing at locating the real positions, for any such law and not only the uniform one. We are explicit that this is a condition Alice enforces, that it is distinct from steganalytic undetectability, and that the latter requires the embedded law to match the declared service's legitimate-input law.

cs.CR↗

arXiv2026

A Stackelberg Model for Hybridization in Cryptography

Similar to a strategic interaction between rational and intelligent agents, cryptography problems can be examined through the prism of game theory. In this setting, the agent aiming to protect a message is called the defender, while the one attempting to decrypt it, generally for malicious purposes, is the attacker. To strengthen security in cryptography, various strategies have been developed, among which hybridization stands out as a key concept in modern cryptographic design. This strategy allows the defender to select among different encryption algorithms (classical, post-quantum, or hybrid) while carefully balancing security and operational costs. On the other side, the attacker, limited by available resources, chooses cryptanalysis methods capable of breaching the selected algorithm. We model this interaction as a Stackelberg cryptographic hybridization problem under resource constraints. Here, the defender randomizes over encryption algorithms, and the attacker observes the choice before selecting suitable cryptanalysis methods. The attacker's decision is framed as a conditional optimization problem, which we refer to as the ``attacker subgame''. We then propose a dynamic programming approach for the attacker's subgame, while the defender's Stackelberg optimization is formulated as a linear program.

cs.CR↗

arXiv2026

Post-Quantum Sanitizable Signatures from McEliece-Based Chameleon Hashing

We introduce a novel post-quantum sanitizable signature scheme constructed upon a chameleon hash function derived from the McEliece cryptosystem. In this design, the designated sanitizer possesses the inherent trapdoor of a Goppa code, which facilitates controlled collision-finding via Patterson decoding. This mechanism enables authorized modification of specific message blocks while ensuring all other content remains immutably bound. We provide formal security definitions and rigorous proofs of existential unforgeability and immutability, grounded in the hardness of syndrome decoding in the random-oracle model, where a robust random oracle thwarts trivial linear hash collisions. A key innovation lies in our precise characterization of the transparency property: by imposing a specific weight constraint on the randomizers generated by the signer, we achieve perfect transparency, rendering sanitized signatures indistinguishable from freshly signed ones. This work establishes the first transparent, code-based, post-quantum sanitizable signature scheme, offering strong theoretical guarantees and a pathway for practical deployment in long-term secure applications.

cs.CR↗

arXiv2024

Predicting Traffic Flow with Federated Learning and Graph Neural with Asynchronous Computations Network

Real-time traffic flow prediction holds significant importance within the domain of Intelligent Transportation Systems (ITS). The task of achieving a balance between prediction precision and computational efficiency presents a significant challenge. In this article, we present a novel deep-learning method called Federated Learning and Asynchronous Graph Convolutional Network (FLAGCN). Our framework incorporates the principles of asynchronous graph convolutional networks with federated learning to enhance the accuracy and efficiency of real-time traffic flow prediction. The FLAGCN model employs a spatial-temporal graph convolution technique to asynchronously address spatio-temporal dependencies within traffic data effectively. To efficiently handle the computational requirements associated with this deep learning model, this study used a graph federated learning technique known as GraphFL. This approach is designed to facilitate the training process. The experimental results obtained from conducting tests on two distinct traffic datasets demonstrate that the utilization of FLAGCN leads to the optimization of both training and inference durations while maintaining a high level of prediction accuracy. FLAGCN outperforms existing models with significant improvements by achieving up to approximately 6.85% reduction in RMSE, 20.45% reduction in MAPE, compared to the best-performing existing models.

cs.LG↗

arXiv2024

EZ-CLIP: Efficient Zeroshot Video Action Recognition

Recent advancements in large-scale pre-training of visual-language models on paired image-text data have demonstrated impressive generalization capabilities for zero-shot tasks. Building on this success, efforts have been made to adapt these image-based visual-language models, such as CLIP, for videos extending their zero-shot capabilities to the video domain. While these adaptations have shown promising results, they come at a significant computational cost and struggle with effectively modeling the crucial temporal aspects inherent to the video domain. In this study, we present EZ-CLIP, a simple and efficient adaptation of CLIP that addresses these challenges. EZ-CLIP leverages temporal visual prompting for seamless temporal adaptation, requiring no fundamental alterations to the core CLIP architecture while preserving its remarkable generalization abilities. Moreover, we introduce a novel learning objective that guides the temporal visual prompts to focus on capturing motion, thereby enhancing its learning capabilities from video data. We conducted extensive experiments on five different benchmark datasets, thoroughly evaluating EZ-CLIP for zero-shot learning and base-to-novel video action recognition, and also demonstrating its potential for few-shot generalization.Impressively, with a mere 5.2 million learnable parameters (as opposed to the 71.1 million in the prior best model), EZ-CLIP can be efficiently trained on a single GPU, outperforming existing approaches in several evaluations.

cs.CV↗

arXiv2023

Metricizing the Euclidean Space towards Desired Distance Relations in Point Clouds

Given a set of points in the Euclidean space $\mathbb{R}^\ell$ with $\ell>1$, the pairwise distances between the points are determined by their spatial location and the metric $d$ that we endow $\mathbb{R}^\ell$ with. Hence, the distance $d(\mathbf x,\mathbf y)=δ$ between two points is fixed by the choice of $\mathbf x$ and $\mathbf y$ and $d$. We study the related problem of fixing the value $δ$, and the points $\mathbf x,\mathbf y$, and ask if there is a topological metric $d$ that computes the desired distance $δ$. We demonstrate this problem to be solvable by constructing a metric to simultaneously give desired pairwise distances between up to $O(\sqrt\ell)$ many points in $\mathbb{R}^\ell$. We then introduce the notion of an $\varepsilon$-semimetric $\tilde{d}$ to formulate our main result: for all $\varepsilon>0$, for all $m\geq 1$, for any choice of $m$ points $\mathbf y_1,\ldots,\mathbf y_m\in\mathbb{R}^\ell$, and all chosen sets of values $\{δ_{ij}\geq 0: 1\leq i<j\leq m\}$, there exists an $\varepsilon$-semimetric $\tildeδ:\mathbb{R}^\ell\times \mathbb{R}^\ell\to\mathbb{R}$ such that $\tilde{d}(\mathbf y_i,\mathbf y_j)=δ_{ij}$, i.e., the desired distances are accomplished, irrespectively of the topology that the Euclidean or other norms would induce. We showcase our results by using them to attack unsupervised learning algorithms, specifically $k$-Means and density-based (DBSCAN) clustering algorithms. These have manifold applications in artificial intelligence, and letting them run with externally provided distance measures constructed in the way as shown here, can make clustering algorithms produce results that are pre-determined and hence malleable. This demonstrates that the results of clustering algorithms may not generally be trustworthy, unless there is a standardized and fixed prescription to use a specific distance function.

cs.CG↗

arXiv2019

Localization of Unmanned Aerial Vehicles in Corridor Environments using Deep Learning

Vision-based pose estimation of Unmanned Aerial Vehicles (UAV) in unknown environments is a rapidly growing research area in the field of robot vision. The task becomes more complex when the only available sensor is a static single camera (monocular vision). In this regard, we propose a monocular vision assisted localization algorithm, that will help a UAV to navigate safely in indoor corridor environments. Always, the aim is to navigate the UAV through a corridor in the forward direction by keeping it at the center with no orientation either to the left or right side. The algorithm makes use of the RGB image, captured from the UAV front camera, and passes it through a trained deep neural network (DNN) to predict the position of the UAV as either on the left or center or right side of the corridor. Depending upon the divergence of the UAV with respect to the central bisector line (CBL) of the corridor, a suitable command is generated to bring the UAV to the center. When the UAV is at the center of the corridor, a new image is passed through another trained DNN to predict the orientation of the UAV with respect to the CBL of the corridor. If the UAV is either left or right tilted, an appropriate command is generated to rectify the orientation. We also propose a new corridor dataset, named NITRCorrV1, which contains images as captured by the UAV front camera when the UAV is at all possible locations of a variety of corridors. An exhaustive set of experiments in different corridors reveal the efficacy of the proposed algorithm.

cs.CV↗