The widespread adoption of consumer electronics (CE), particularly in smart home ecosystems, has led to the proliferation of intelligent devices such as smart cameras, video doorbells, and smart door locks. While these devices enhance convenience and automation, they generate massive amounts of visual data containing sensitive personal information, thereby increasing the risk of privacy leakage and challenging traditional encryption methods in consumer IoT networks. To address this issue, a cryptanalysis-driven image encryption scheme via a chaos-based 3D S-box and genetic code is proposed, specifically designed to safeguard visual privacy in consumer applications. First, a novel dynamical architecture, formally termed 3D Non-degenerate Hyperchaos (3D-NDHC), is constructed to serve as a robust entropy source. Explicitly defined as a globally bounded volume-expanding hyperchaotic system, 3D-NDHC distinguishes itself from traditional dissipative maps by exhibiting global robust hyperchaos and scalable complexity, where the Lyapunov exponents grow logarithmically with control parameters, ensuring high unpredictability. Next, a dynamically generated three-dimensional S-box driven by these chaotic sequences is introduced to perform efficient image permutation. Subsequently, a genetic code method enhanced by cryptanalysis is applied to confuse the permuted image matrix, specifically addressing the security vulnerabilities often inherent in lightweight encryption algorithms. Finally, under the control of pseudo-chaotic sequences, a lightweight wave diffusion process is employed to spread the pixel values and produce the final ciphertext image. In contrast to conventional algorithms that rely on standard encryption techniques, the proposed approach integrates a chaos-driven 3D S-box with a cryptanalysis-enhanced genetic code scheme. This combination introduces a high level of confusion and diffusion, significantly increasing the difficulty for attackers attempting to illegally decrypt sensitive images from smart devices. Experimental results and security analyses demonstrate that the scheme effectively resists various cryptographic attacks and outperforms several recently proposed image encryption algorithms, offering a robust solution for protecting digital image privacy in smart home environments.

With the advancement of the Internet of Things (IoT) and communication technologies, multimedia information has become a significant component of communication. Image data, as a carrier with high information density and large transmission volume, is increasingly at risk of information leakage. To address the issue of digital private image leakage, this paper proposes a reliable image security protection scheme based on non-degenerate hyper-chaotic systems for dynamic permutation and single-round combined diffusion. By leveraging the high dynamic properties of non-degenerate chaos, the scheme effectively resolves the insufficient sensitivity of pseudo-random sequences to initial values. Additionally, the single-round combined double-diffusion system significantly enhances the encryption efficiency of the algorithm. Experimental and theoretical verification demonstrate that the algorithm exhibits high sensitivity and effective robustness, capable of resisting common attacks and providing a reliable security solution for communication and IoT.

The rapid development of communication technology has significantly improved information transmission and increased capacity. In the context of the Internet of Things (IoT), where massive visual data transmission faces challenges of real-time processing and security threats. To address this problem, this article proposes a novel encryption algorithm based on a 3-D S-box combined with fractal-sort-matrix and Fibonacci $Q$ -matrix (3DSFF). To address the shortcomings of traditional S-box encryption, this article integrates the 3-D S-box with the fractal-sortmatrix (FSM), thus enhancing the uncertainty of permutation and transformation. In addition, to overcome the limitation of using a single value inciting Fibonacci $Q$ -matrix (FQM) applications, this article combines FQM with chaotic sequences to strengthen its resistance against exhaustive attacks. Through comprehensive experimental tests on the algorithm’s outcomes, it demonstrates notable improvements over previous algorithms, achieving an average information entropy of 7.9993 and a correlation coefficient close to 0.01 after encryption. These tests indicate that the scheme can withstand common attacks, making it a sufficiently secure solution for the confidentiality of private images. Moreover, these advances provide a secure and efficient visual data protection framework for IoT applications involving antitheft surveillance, data acquisition, and communication transmission.

Chaos-based image cryptosystems often produce noise-like ciphertexts, but dynamical complexity does not establish security. This paper presents a structural cryptanalysis of the fractional-order memristive Hopfield neural network (FMHNN)-based medical-image cryptosystem proposed by Sun et al. For fixed parameters and dimensions, the published data path is formalized as a plaintext-independent permutation followed by a fixed exclusive-or (XOR) mask. Chosen-plaintext attack (CPA), known-plaintext attack (KPA), chosen-ciphertext attack (CCA), and fixed-key differential analyses then derive equivalent-secret recovery, leakage, and malleability relations. CPA and CCA recover the complete equivalent secret in $1+\lceil {\log }_{256}N\rceil$ queries for an N-pixel image; KPA resolves positions with unique cross-image signatures under secret reuse. Numerical validation, including a black-box encryption-oracle experiment, confirms the recovery relations. On the public BreastMNIST+ 128 benchmark, complete KPA recovery occurs at $m=7$, and the recovered equivalent secret decrypts an independent held-out image exactly. The analysis shows that favorable entropy, correlation, number-of-pixels change rate (NPCR), and unified average changing intensity (UACI) cannot substitute for structural cryptanalysis. Attack-guided module-level countermeasures are formulated to provide per-image diversification, feedback diffusion, and integrity verification, and are evaluated through exact decryption and integrity checks. The study offers an attack-based assessment and design guidance for chaos-based image-encryption systems.

Chaos-based image ciphers may exhibit favorable statistical outputs while retaining exploitable algebraic relations. Following Kerckhoffs’s principle, this paper analyzes a published memristive Hopfield neural network color image cryptosystem as a public algorithm with secret initial conditions. We show that its image-layer transformation is exactly a plaintext-independent permutation followed by a reusable bytewise XOR mask shared by the RGB channels; the induced permutation and mask form an equivalent decryption secret. Based on this reduction, we develop chosen-plaintext, known-plaintext, chosen-ciphertext, differential, and black-box analyses, then validate complete recoveries by exact decryption of an independent challenge image. Next, we introduce target-specific modifications based on counter-dependent HNN state derivation, independent channel permutation material, and bidirectional plaintext-dependent state feedback diffusion. Operation-level inversion and experiments on the redesigned image layer verify exact round-trip recovery, channel separation, counter-dependent ciphertexts, and broad propagation of a controlled plaintext change. The proposed improved method effectively resists the cryptanalytic attacks studied in this paper.

Chaos-based image encryption schemes are often motivated by the apparent randomness of chaotic sequences and by the nonlinear processing capability of convolutional neural networks (CNNs). However, these properties alone do not guarantee cryptographic security. In this work, we present a systematic cryptanalysis of an image-encryption framework combining Logistic-map dynamics, pixel permutation, XOR masking, CNN-based processing, image hiding, and wavelet-domain operations. Under the fixed-state condition considered in our analysis, the reversible encryption core can be reduced to $\mathbf{c}=\Pi \left(\mathbf{p}\right)\oplus \mathbf{s}$, where $\Pi$ is a plaintext-independent permutation and $\mathbf{s}$ is a fixed byte-wise mask. The effective secret is therefore characterized by the equivalent pair $(\Pi ,\mathbf{s})$, rather than by the nominal parameters of the underlying chaotic map. Based on this structural reduction, we derive mask-cancellation and differential relations and develop five complementary cryptanalytic methods: chosen-plaintext, known-plaintext, chosen-ciphertext, differential, and model-class black-box attacks. The chosen-plaintext attack recovers the XOR mask from an all-zero query and determines the permutation using position-labelled inputs. The known-plaintext attack constructs cross-sample XOR fingerprints to progressively identify permutation positions when the available plaintexts provide sufficient diversity. The chosen-ciphertext and differential attacks recover the inverse mapping or eliminate the fixed mask through carefully designed query pairs. The black-box attack further uses multi-byte coordinate encoding to identify the complete fixed input–output transformation without prior knowledge of the block structure. These attacks do not require recovery of the Logistic-map parameters; instead, they directly reconstruct the equivalent transformation required to decrypt ciphertexts generated under the same induced state. In addition, for the explicitly linear fixed-convolution component of the target framework, we perform a separate conditional impulse-response experiment to recover its effective convolution kernel. Experiments on $128\times 128$ grayscale images with $16\times 16$ blocks demonstrate exact byte-level plaintext recovery for the analyzed fixed-state reversible core, while also showing that known-plaintext recovery depends on sample diversity. The results indicate that reusable and separable permutation–mask structures remain vulnerable even when embedded in chaos- and CNN-assisted image-processing pipelines, motivating per-image state diversification and stronger key-dependent coupling between permutation and diffusion.