Phishing attacks represents one of the primary attack methods which is used by cyber attackers. In many cases, attackers use deceptive emails along with malicious attachments to trick users into giving away sensitive information or installing malware while compromising entire systems. The flexibility of malicious email attachments makes them stand out as a preferred vector for attackers as they can embed harmful content such as malware or malicious URLs inside standard document formats. Although phishing email defenses have improved a lot, attackers continue to abuse attachments, enabling malicious content to bypass security measures. Moreover, another challenge that researches face in training advance models, is lack of an unified and comprehensive dataset that covers the most prevalent data types. To address this gap, we generated CIC-Trap4Phish, a multi-format dataset containing both malicious and benign samples across five categories commonly used in phishing campaigns: Microsoft Word documents, Excel spreadsheets, PDF files, HTML pages, and QR code images. For the first four file types, a set of execution-free static feature pipeline was proposed, designed to capture structural, lexical, and metadata-based indicators without the need to open or execute files. Feature selection was performed using a combination of SHAP analysis and feature importance, yielding compact, discriminative feature subsets for each file type. The selected features were evaluated by using lightweight machine learning models, including Random Forest, XGBoost, and Decision Tree. All models demonstrate high detection accuracy across formats. For QR code-based phishing (quishing), two complementary methods were implemented: image-based detection by employing Convolutional Neural Networks (CNNs) and lexical analysis of decoded URLs using recent lightweight language models.

网络钓鱼攻击是网络攻击者使用的主要攻击方法之一。在许多情况下，攻击者使用欺骗性电子邮件和恶意附件来诱骗用户泄露敏感信息或安装恶意软件，同时危及整个系统。恶意电子邮件附件的灵活性使其成为攻击者的首选载体，因为它们可以在标准文档格式中嵌入有害内容，例如恶意软件或恶意 URL。尽管网络钓鱼电子邮件防御已经有了很大改进，但攻击者仍在继续滥用附件，使恶意内容能够绕过安全措施。此外，研究人员在训练高级模型时面临的另一个挑战是缺乏涵盖最流行数据类型的统一且全面的数据集。为了弥补这一差距，我们生成了 CIC-Trap4Phish，这是一个多格式数据集，其中包含网络钓鱼活动中常用的五个类别的恶意和良性样本：Microsoft Word 文档、Excel 电子表格、PDF 文件、HTML 页面和 QR 代码图像。对于前四种文件类型，提出了一组免执行静态特征管道，旨在捕获结构、词汇和基于元数据的指标，而无需打开或执行文件。结合 SHAP 分析和特征重要性来执行特征选择，为每种文件类型生成紧凑的、有区别的特征子集。使用轻量级机器学习模型（包括随机森林、XGBoost 和决策树）对所选特征进行评估。所有模型都表现出跨格式的高检测精度。对于基于 QR 码的网络钓鱼（quishing），实施了两种互补的方法：使用卷积神经网络 (CNN) 进行基于图像的检测，以及使用最新的轻量级语言模型对解码的 URL 进行词法分析。

Diffusion models have achieved remarkable generation quality, but they suffer from significant inference cost due to their reliance on multiple sequential denoising steps, motivating recent efforts to distill this inference process into a few-step regime. However, existing distillation methods typically approximate the teacher trajectory by using linear shortcuts, which makes it difficult to match its constantly changing tangent directions as velocities evolve across timesteps, thereby leading to quality degradation. To address this limitation, we propose ArcFlow, a few-step distillation framework that explicitly employs non-linear flow trajectories to approximate pre-trained teacher trajectories. Concretely, ArcFlow parameterizes the velocity field underlying the inference trajectory as a mixture of continuous momentum processes. This enables ArcFlow to capture velocity evolution and extrapolate coherent velocities to form a continuous non-linear trajectory within each denoising step. Importantly, this parameterization admits an analytical integration of this non-linear trajectory, which circumvents numerical discretization errors and results in high-precision approximation of the teacher trajectory. To train this parameterization into a few-step generator, we implement ArcFlow via trajectory distillation on pre-trained teacher models using lightweight adapters. This strategy ensures fast, stable convergence while preserving generative diversity and quality. Built on large-scale models (Qwen-Image-20B and FLUX.1-dev), ArcFlow only fine-tunes on less than 5% of original parameters and achieves a 40x speedup with 2 NFEs over the original multi-step teachers without significant quality degradation. Experiments on benchmarks show the effectiveness of ArcFlow both qualitatively and quantitatively.

扩散模型已经实现了卓越的生成质量，但由于依赖多个连续的去噪步骤，它们的推理成本很高，这促使人们最近努力将这种推理过程提炼为几个步骤。然而，现有的蒸馏方法通常使用线性捷径来近似教师轨迹，这使得随着速度跨时间步长的变化而难以匹配其不断变化的切线方向，从而导致质量下降。为了解决这个限制，我们提出了 ArcFlow，这是一种分步蒸馏框架，它明确地采用非线性流动轨迹来近似预先训练的教师轨迹。具体来说，ArcFlow 将推理轨迹下的速度场参数化为连续动量过程的混合。这使得 ArcFlow 能够捕获速度演化并推断相干速度，以在每个去噪步骤内形成连续的非线性轨迹。重要的是，这种参数化允许对非线性轨迹进行分析积分，从而避免数值离散误差并导致教师轨迹的高精度近似。为了将这种参数化训练成几步生成器，我们使用轻量级适配器在预先训练的教师模型上通过轨迹蒸馏来实现 ArcFlow。该策略确保快速、稳定的收敛，同时保持生成多样性和质量。 ArcFlow 基于大型模型（Qwen-Image-20B 和 FLUX.1-dev）构建，仅对不到 5% 的原始参数进行微调，与原始多步教师相比，通过 2 个 NFE 实现了 40 倍的加速，而质量没有显着下降。基准实验从定性和定量两个方面证明了 ArcFlow 的有效性。