Binary Code Similarity Detection is a method that involves comparing two or more binary code segments to identify their similarities and differences. This technique plays a crucial role in areas such as software security, vulnerability detection, and software composition analysis. With the extensive use of binary code in software development and system optimization, binary code similarity detection has become an important area of research. Traditional methods of source code similarity detection face challenges when dealing with the unreadable and complex nature of binary code, necessitating specialized techniques and algorithms. This review compares and summarizes various techniques and methods of binary code similarity detection, highlighting their strengths and limitations in handling different characteristics of binary code. Additionally, the article suggests potential future research directions. As research and innovation in this technology continue to advance, binary code similarity detection is expected to play an increasingly significant role in fields like software security.

In digital security, Reversible Adversarial Examples (RAE) blend adversarial attacks with Reversible Data Hiding (RDH) within images to thwart unauthorized access. Traditional RAE methods, however, compromise attack efficiency for the sake of perturbation concealment, diminishing the protective capacity of valuable perturbations and limiting applications to white-box scenarios. This paper proposes a novel Dual-Phase merging Reversible Adversarial Example (DP-RAE) generation framework, combining a heuristic black-box attack and RDH with Grayscale Invariance (RDH-GI) technology. This dual strategy not only evaluates and harnesses the adversarial potential of past perturbations more effectively but also guarantees flawless embedding of perturbation information and complete recovery of the original image. Experimental validation reveals our method's superiority, secured an impressive 96.9% success rate and 100% recovery rate in compromising black-box models. In particular, it achieved a 90% misdirection rate against commercial models under a constrained number of queries. This marks the first successful attempt at targeted black-box reversible adversarial attacks for commercial recognition models. This achievement highlights our framework's capability to enhance security measures without sacrificing attack performance. Moreover, our attack framework is flexible, allowing the interchangeable use of different attack and RDH modules to meet advanced technological requirements.

Return-oriented programming(ROP) is a prevalent code reuse technique that hijacks a program’s control flow by modifying its return addresses on the stack. Researchers have proposed some return address verification methods by using the message authentication code(MAC). But these approaches suffer from high performance overhead. In this paper, we first propose Dislocated Stack, a new kind of stack layout in which a previous return address would be pushed onto the current stack frame and the current return address would be stored into a hardware buffer on function calls. Based on Dislocated Stack, we design two new verification approaches, Lazy Verification and Batch Verification. Lazy Verification does not verify a return address popped from stack until it is going to be used for return. Batch Verification verifies a couple of return addresses at one time. We implemented these two designs on RISC-V architecture and quantitatively analysed their effect on QEMU. Our experiments show that Lazy Verification reduces over 99% verifications on function returns and incurs only 1.23% performance overhead; while in Batch Verification, the overhead is merely 0.78%. The result demonstrates that these two approaches are highly efficient for return address verification.

Code classification and code clone detection are crucial for understanding and maintaining large software systems. Although deep learning surpasses traditional techniques in capturing the features of source code, existing models suffer from low processing power and high complexity. We propose a novel source code representation method based on the multi-head attention mechanism (SCRMHA). SCRMHA captures the vector representation of entire code segments, enabling it to focus on different positions of the input sequence, capture richer semantic information, and simultaneously process different aspects and relationships of the sequence. Moreover, it can calculate multiple attention heads in parallel, speeding up the computational process. We evaluate SCRMHA on both the standard dataset and an actual industrial dataset, and analyze the differences between these two datasets. Experiment results in code classification and clone detection tasks show that SCRMHA consumes less time and reduces complexity by about one-third compared with traditional source code feature representation methods. The results demonstrate that SCRMHA reduces the computational complexity and time consumption of the model while maintaining accuracy.

As audio adversarial attacks continue to evolve, Automatic Speech Recognition (ASR) models have emerged as a significant target. Traditional audio attack methods often focus on minimizing perturbation magnitude and frequency, overlooking the importance of perturbation location. However, certain audio regions hold lower importance for ASR models, making attacks on these regions less effective and more perceptible as noise. Additionally, the human ear perceives noise differently depending on its placement within the audio sequence, with noise in silent segments being more noticeable. To address these challenges, this paper proposes Pitch Sparse Audio Attack (PASK), an innovative framework designed to enhance adversarial imperceptibility through sparse perturbations. PASK introduces two key techniques: Pitch Mapping, which provides a strategic starting point for perturbation, and an adaptive grouped selective mask that achieves targeted sparsity, focusing perturbations on high-impact audio regions. Experimental results demonstrate that PASK outperforms existing methods in both effectiveness and imperceptibility. Furthermore, a human study confirms that silent-segment perturbations are more easily detected, underscoring the perceptual advantages of our approach.

The rapid advancements in artificial intelligence have significantly accelerated the adoption of speech recognition technology, leading to its widespread integration across various applications. However, this surge in usage also highlights a critical issue: audio data is highly vulnerable to unauthorized exposure and analysis, posing significant privacy risks for businesses and individuals. This paper introduces an Information-Obfuscation Reversible Adversarial Example (IO-RAE) framework, the pioneering method designed to safeguard audio privacy using reversible adversarial examples. IO-RAE leverages large language models to generate misleading yet contextually coherent content, effectively preventing unauthorized eavesdropping by humans and Automatic Speech Recognition (ASR) systems. Additionally, we propose the Cumulative Signal Attack technique, which mitigates high-frequency noise and enhances attack efficacy by targeting low-frequency signals. Our approach ensures the protection of audio data without degrading its quality or our ability. Experimental evaluations demonstrate the superiority of our method, achieving a targeted misguidance rate of 96.5% and a remarkable 100% untargeted misguidance rate in obfuscating target keywords across multiple ASR models, including a commercial black-box system from Google. Furthermore, the quality of the recovered audio, measured by the Perceptual Evaluation of Speech Quality score, reached 4.45, comparable to high-quality original recordings. Notably, the recovered audio processed by ASR systems exhibited an error rate of 0%, indicating nearly lossless recovery. These results highlight the practical applicability and effectiveness of our IO-RAE framework in protecting sensitive audio privacy.