Image synthesis has become a central problem in generative AI, with applications spanning virtual reality, medical imaging, autonomous systems, and creative content generation. Diffusion-based generative models have substantially advanced the field by producing high-fidelity, visually consistent outputs, yet a fundamental tension remains: hybrid synthesis tasks require photorealism and geometric consistency to be achieved together, so that perceptual quality does not come at the cost of structural accuracy. This review surveys recent progress in conditional diffusion frameworks, with particular attention to geometry-aware conditioning mechanisms that connect appearance and structure. Our contributions are a taxonomy of 18 conditioning frameworks, a six-parameter controllability analysis (Table II), formal definitions of the evaluation metrics most commonly reported in the literature (FID, SSIM, LPIPS, IS, Precision, Recall, Dice) with a consolidated reference table, an explicit architectural comparison of DDPM, DDIM, and LDM together with transformer-based generative models (DiT, VQGAN+Transformer), a structured comparison of diffusion models and GANs across deployment-relevant criteria, and a discussion of open challenges that includes the limitations of current experimental validation practice.
Kanika Verma, Khushi Aggarwal, Pradipti Singh et al.· International Conference Com...· 0 citations
This study investigated the robustness of deep learning-based Network Intrusion Detection Systems (NIDS) against adversarial attacks by proposing a confidence-aware adaptive defense framework. The proposed approach integrates a baseline feedforward neural network, an adversarially trained robust model, and an adversarial detector to dynamically select the most appropriate prediction path based on detector confidence. Experimental evaluation under single-step, multi-step, and adaptive adversarial attack scenarios demonstrated that the framework significantly improves detection robustness while maintaining high classification accuracy on clean network traffic. The adaptive fusion strategy effectively mitigates the impact of adversarial perturbations, reducing misclassification rates and enhancing the reliability of intrusion detection in dynamic cybersecurity environments. These findings confirm that confidence-guided adaptive defense mechanisms provide a practical solution for strengthening the resilience of AI-driven NIDS against evolving attack strategies. However, the proposed framework was evaluated using controlled experimental settings and specific attack models, which may not fully represent the diversity of real-world cyber threats. Future work will focus on validating the framework in large-scale operational networks, extending it to advanced zero-day and adaptive attacks, and investigating lightweight deployment strategies for real-time edge and cloud-based cybersecurity applications.
Aastha Ahlawat, Anurag Goel· 2026 4th International Confe...· 0 citations