Protecting the ownership of pictures and the integrity of copyright law is now of paramount importance with the explosion in digital content, especially content that is vulnerable to manipulation and distribution without the copyright owner's permission. This research work combines post-quantum cryptography, Artificial intelligence (AI) and quantum computing aspects to introduce ground breaking quantum intelligent and secure architecture for image watermarking. The proposed system is using quantum transform methods for inserting undetectable and tamper- proof quantum watermarks into digital images. AI models are then applied to the images for change detection and immediate verification of the authenticity of the watermark. Therefore, quantum key distribution (QKD), lattice-based post-quantum cryptography and other quantum safe encryption schemes are employed to cryptographically secure the metadata of watermark for good cryptographic security. This hybrid approach has been shown to enhance the security against both classical and quantum attacks. NFT authentication, secure cloud storage of pictures, and protection for copyright in digital media are examples of some potential applications. Simulation and experimental results show increased strength of the watermark, accuracy of message about unauthorized insertion of the watermark, and enhanced cryptographic security with respect to all other methods. The article demonstrates how AI can be aligned with quantum computing in order to secure audio-visual content in post-quantum cyber security.
Protecting the ownership of pictures and the integrity of copyright law is now of paramount importance with the explosion in digital content, especially content that is vulnerable to manipulation and distribution without the copyright owner's permission. This research work combines post-quantum cryptography, Artificial intelligence (AI) and quantum computing aspects to introduce ground breaking quantum intelligent and secure architecture for image watermarking. The proposed system is using quantum transform methods for inserting undetectable and tamper- proof quantum watermarks into digital images. AI models are then applied to the images for change detection and immediate verification of the authenticity of the watermark. Therefore, quantum key distribution (QKD), lattice-based post-quantum cryptography and other quantum safe encryption schemes are employed to cryptographically secure the metadata of watermark for good cryptographic security. This hybrid approach has been shown to enhance the security against both classical and quantum attacks. NFT authentication, secure cloud storage of pictures, and protection for copyright in digital media are examples of some potential applications. Simulation and experimental results show increased strength of the watermark, accuracy of message about unauthorized insertion of the watermark, and enhanced cryptographic security with respect to all other methods. The article demonstrates how AI can be aligned with quantum computing in order to secure audio-visual content in post-quantum cyber security.
Quantum computers pose existential threats to classical cryptographic systems, including those used to secure digital watermarking—the embedding of robust, imperceptible information into multimedia for copyright protection and authentication. As Shor's and Grover's algorithms undermine the security of RSA, ECC, and symmetric key schemes, respectively, watermarking techniques reliant on these cryptosystems are at risk of attack once practical quantum computers emerge. This paper reviews the state-of-the-art in quantum-resistant, or post-quantum, digital watermarking. We examine approaches that leverage post-quantum public-key primitives, such as lattice-based, hash-based, and code-based cryptography, to establish secure embedding and verification protocols that are immune to quantum decryption. Additionally, we analyze the computational trade-offs of integrating quantum-safe algorithms into watermarking pipelines—in terms of embedding transparency, capacity, computational cost, and resilience against attacks.
Classical cryptographic have a threat because of the emergence of quantum computing, and hence there is an urgent need to research post-quantum cryptography. To evaluate the potential of post-quantum encryption algorithms in countering quantum threats, here the study sees how they can be integrated with artificial intelligence (AI) and machine learning (ML). For their theoretical quantum resistance, the study looks at established post-quantum cryptography methods such as lattice-based, hash-based, and multivariate cryptographic schemes. A review of current research also simulation-based research is also taken into account to review the potential of AI/ML in bolstering security protocols and simplifying encryption processes. The key takeaway is that machine learning and AI do have actual potential to enhance post-quantum encryption through increasing its efficiency, flexibility, and resistance to future quantum attacks. AI also assists in real-time responsiveness in avoiding possible vulnerabilities. Our work contributes to the growing literature by combining insights from cryptographic and AI/ML research, providing a comprehensive evaluation of post-quantum encryption methods. This study emphasizes the highest importance of adopting hybrid cryptography techniques in order to advance information security in the quantum era, leading to further research in practical applications.
Quantum Watermarking (QWM) is an emerging field that combines quantum information theory with digital watermarking techniques to enhance the security of digital content. By leveraging quantum properties such as superposition, entanglement, and the no-cloning theorem, QWM provides robust protection against unauthorised access, tampering, and duplication—offering a significant advantage over classical methods. As the volume and value of digital data grow, the need for advanced intellectual property protection becomes increasingly vital, making QWM a promising approach to next-generation data security. This paper presents a comprehensive review of the theoretical foundations of QWM, its core principles, and various classification methods based on quantum states, embedding techniques, and application domains such as authentication, copyright protection, and tamper detection. It also explores the mathematical models underlying QWM, including quantum state representations and error correction protocols, along with recent advances in experimental validations. Despite its potential, QWM faces challenges such as noise sensitivity, scalability limitations, and high computational resource requirements. Issues related to secure key management and implementation vulnerabilities also remain. Nevertheless, this review aims to serve as a foundational reference for researchers and practitioners, highlighting the promise of QWM in reshaping digital content security, particularly in sensitive domains like digital forensics and healthcare.
The rapid advancement of quantum computing poses a significant threat to the confidentiality and integrity of classified and defense communications. Quantum algorithms have the potential to undermine widely deployed public key cryptographic schemes, exposing sensitive military and governmental information to interception and decryption. Foundational work in quantum cryptography has demonstrated that the principles of quantum mechanics can be leveraged to achieve fundamentally secure key distribution, offering a promising countermeasure to quantum-enabled attacks (Bennett & Brassard, 2014; Ekert, 1991). As defense communication systems increasingly rely on longterm data confidentiality, the urgency of transitioning toward quantum-resilient security architectures has become a strategic priority. Despite their widespread adoption, classical cryptographic mechanisms are increasingly vulnerable in the post-quantum era. Studies on post-quantum cryptography have shown that many conventional encryption and key exchange algorithms are susceptible to quantum adversaries, necessitating the development and standardization of new cryptographic primitives (Chen et al., 2016). Ongoing standardization efforts further highlight the complexity of migrating large-scale, mission-critical systems to quantum-safe solutions, particularly within defense environments where interoperability, reliability, and assurance requirements are stringent (Alagic et al., 2025). In parallel, artificial intelligence has emerged as a powerful enabler of advanced security analytics. Machine learning and deep learning techniques enhance intrusion detection, anomaly identification, and adaptive response capabilities within complex communication infrastructures (Buczak & Guven, 2016). Recent research also demonstrates that AI can be applied directly within quantum cryptographic systems, for example by improving signal processing, error correction, and performance optimization in quantum key distribution implementations (Chin et al., 2021). The convergence of quantum cryptography and AIdriven analytics therefore offers a complementary and proactive approach to securing defense communications against both classical and quantum threats. This paper analyzes the integration of quantum cryptography and artificial intelligence as a unified framework for protecting classified and defense communications. It synthesizes advances in quantum key distribution, post-quantum cryptography, and AI-based security analytics, and proposes an integrated perspective that enhances resilience, detection capability, and operational robustness. The study highlights key implications for defense communication systems, emphasizing how AI-enhanced quantum security architectures can support long-term confidentiality, adaptive defense, and strategic cyber resilience in the evolving threat landscape (Sarker et al., 2021).
As artificial intelligence (AI) systems increasingly underpin critical applications in healthcare, finance, defense, and infrastructure, ensuring their security has become paramount. However, the rapid advancement of quantum computing poses a significant threat to classical cryptographic schemes that currently safeguard these systems. Quantum algorithms such as Shor’s and Grover’s threaten to undermine the confidentiality, integrity, and authentication of data and models that AI systems depend on. In this context, Post-Quantum Cryptography (PQC) has emerged as a crucial area of research aimed at developing cryptographic algorithms that are secure against both classical and quantum adversaries. This paper investigates the intersection of quantum computing threats and AI system vulnerabilities, highlighting how current security mechanisms may fail in a post-quantum era. We explore various categories of PQC—including lattice-based, hash-based, and multivariate polynomial cryptography—and evaluate their applicability to securing AI systems, particularly in scenarios such as model distribution, federated learning, data encryption, and communication in AI-enabled edge and IoT environments. Through analysis of emerging use cases and proposed architectures, the paper outlines how PQC can be integrated into the AI lifecycle to mitigate quantum-era threats. It also identifies key challenges such as computational overhead, integration complexity, and standardization gaps. The study concludes by outlining future research opportunities, including the development of efficient PQC protocols tailored to AI workloads, the creation of quantum-resilient AI pipelines, and the importance of interdisciplinary collaboration to secure AI’s future.
The rapid advancement of quantum computing threatens the foundations of classical cryptography, creating severe risks for resource-constrained IoT systems widely used in healthcare, autonomous vehicles, and industrial automation. Conventional algorithms like RSA and ECC are vulnerable to Shor's and Grover's algorithms, exposing IoT to passive and active quantum-based attacks. This manuscript proposes a quantum-safe framework integrating Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD), and Artificial Intelligence (AI) to ensure confidentiality, integrity, and resilience. The framework leverages lattice, hash, and code-based PQC, lightweight cryptographic protocols, hybrid security models, and efficient key management to protect IoT communications. By combining PQC, QKD, and AI-driven adaptive security, the framework offers scalable, sustainable, and future-proof protection in the post-quantum era.