Many non-verbal individuals depend on symbol-based communication, yet AAC devices are frequently abandoned because they are bulky, conspicuous, or hard to reconfigure. Cue Clear addresses this with a wearable display that cycles through caregiver-curated communication symbols, controlled by a companion smartphone app. The system pairs a CircuitPython microcontroller driving an ILI9341 LCD—hosting a non-blocking HTTP server over a Wi-Fi access point—with a cross-platform Flutter application that builds symbol “profiles” and activates them on the device [3]. Key challenges included reliable communication with a resource-constrained device, responsive image browsing, and an accessible caregiver interface; these were met with a non-blocking embedded server, a read-through image cache, and a largetarget, confirmation-guarded UI. In testing, caching cut image load latency about 27-fold to 43 ms, and index-only profile activation stayed near 215 ms regardless of profile size. By directly targeting known causes of AAC abandonment, Cue Clear offers a sustainable, low-friction communication aid.
Social-media steganography is a hostile-channel problem because platforms such as Facebook can resize images, recompress them as JPEG, modify chroma information, remove metadata, quantize colors, and apply proprietary optimizations that destroy fragile hidden payloads. This paper presents FBStegNet, an end-to-end deep neural framework for robust binary data hiding in color images transmitted through Facebook-like processing. The key contributions are fourfold: a dense message encoder that distributes payload redundancy across the image, a residual CNN feature extractor that identifies stable cover structures, a differentiable Facebook simulation layer that models randomized JPEG compression, downsampling–upsampling, color quantization, and signal-dependent noise, and a texture-aware attention embedding network that controls where and how strongly information is embedded. By training the en coder and decoder over a stochastic distribution of social-media distortions, FBSteg Net optimizes message recovery after platform processing rather than only preserving clean-image fidelity. In a representative 0.05 bpp case study, FBStegNet reduces the bit-error rate (BER) to 0.018 in simulated Facebook-like tests, compared with 0.482 for LSB matching, 0.214 for DCT robust embedding, and 0.167 for generic neural hiding, while preserving 38.94 dB PSNR and 0.958 SSIM. In real Facebook upload download experiments, it achieves 0.027 BER with 38.51 dB PSNR and 0.951 SSIM, demonstrating that the learned embedding remains recoverable under practical social media transformations. Additionally, this work specifies a reproducible evaluation protocol using steganography image benchmarks, Facebook-style simulated channels, real upload–download tests, payload metrics, perceptual-quality measures, and steganaly sis checks. FBStegNet therefore provides a practical and extensible design for robust secret message communication over lossy social-media image pipelines. Keywords: Information hiding, social media, deep learning, image steganography.
Accurate drone detection in aerial and ground-based surveillance remains difficult because small unmanned aerial vehicles often appear as low-resolution, low-contrast targets embedded in cluttered backgrounds. Their visual appearance changes rapidly with altitude, viewpoint, illumination, motion blur, compression, and weather, causing many general-purpose detectors to lose fine spatial evidence or produce false alarms from birds, towers, clouds, and building structures. This paper proposesed a Multi-Scale Deep Learning Framework (MSDLF) for robust drone detection and classification in complex aerial scenes. The framework integrates a residual convolutional backbone, feature-pyramid aggregation, adaptive scale-attention fusion, decoupled detection heads, and multi scale consistency regularization. Unlike fixed pyramid fusion, the proposed attention module learns image-conditioned weights for features extracted at different strides, allowing the detector to emphasize high-resolution maps for distant drones while preserving deeper semantic context for clutter rejection. Experimental evaluations on VisDrone aerial images and UAV video benchmark datasets demonstrate that MSDLF achieves superior detection performance compared with two-stage, one stage representative, anchorfree, and transformer-based object detection methods. The experimental results of comparison, MSDLF achieves 92.1% precision, 90.4% recall, 91.2% F1-score, 94.7% AP50, and 71.9% mAP, improving over YOLOv8 by 3.4 percentage points in precision, 4.2 points in recall, and 4.1 points in mAP. Ablation and robustness studies further show that multi-scale fusion, adaptive attention, and consistency learning each contribute to higher small-object recall and better stability under blur, low light, compression, and scale reduction. These results indicate that scale-aware attention is an effective and practical mechanism for drone detection systems that must balance accuracy, robustness, and near-real-time inference.
Security Operations Centers rely on detection engineering to convert telemetry from identity systems, endpoints, networks, and applications into actionable security alerts. However, many organizations struggle to align detections with adversary behavior, measure coverage, and maintain detection content at scale. This paper presents a practical ATT&CK-aligned detection engineering framework that combines telemetry assessment, field analysis, detection design, validation, tuning, and continuous monitoring. The framework is demonstrated through case studies covering password spraying, excessive DNS queries and failures, suspicious PowerShell activity, and Detection-as-Code workflows. Each case study maps detection logic to relevant MITRE ATT&CK techniques and discusses data sources, validation methods, tuning considerations, and operational lessons. The paper also compares the proposed approach with traditional indicator-based detection, standalone SIEM rule development, and ATT&CK mapping used only for reporting. The results show that ATT&CK-aligned detection engineering can improve visibility, reduce unmanaged detection gaps, and support scalable security monitoring across healthcare, financial services, e-commerce, education, government, and artificial intelligence environments.
