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摘要:
近年来,随着物联网(Internet of things, IoT)设备的大规模部署,针对物联网设备的恶意代码也不断出现,物联网安全面临来自恶意代码的巨大威胁,亟需对物联网恶意代码检测技术进行综合研究. 随着人工智能(artificial intelligence, AI)在计算机视觉和自然语言处理等领域取得了举世瞩目的成就,物联网安全领域也出现了许多基于人工智能的恶意代码检测工作. 通过跟进相关研究成果,从物联网环境和设备的特性出发,提出了基于该领域研究主要动机的分类方法,从面向物联网设备限制缓解的恶意代码检测和面向性能提升的物联网恶意代码检测2方面分析该领域的研究发展现状. 该分类方法涵盖了物联网恶意代码检测的相关研究,充分体现了物联网设备独有的特性以及当前该领域研究存在的不足. 最后通过总结现有研究,深入讨论了目前基于人工智能的恶意代码检测研究中存在的问题,为该领域未来的研究提出了结合大模型实现物联网恶意代码检测,提高检测模型安全性以及结合零信任架构3个可能的发展方向.
Abstract:In recent years, with the large-scale deployment of Internet of things (IoT) devices, there has been a growing emergence of malicious code targeting IoT devices. IoT security is facing significant threats from malicious code, necessitating comprehensive research on IoT malware detection techniques. Following the remarkable achievements of artificial intelligence (AI) in fields such as computer vision (CV) and natural language processing (NLP), the IoT security field has witnessed numerous efforts in AI-based malware detection as well. By reviewing relevant research findings and considering the characteristics of IoT environments and devices, we propose a classification method for the primary motivations behind research in this field and analyze the research development in IoT malware detection from two perspectives: malware detection techniques towards IoT device limitation mitigation and IoT malware detection techniques towards performance improvement. This classification method encompasses the relevant research in IoT malware detection, which also highlights the unique characteristics of IoT devices and the current limitations of the IoT malware detection field. Finally, by summarizing existing research, we extensively discuss the challenges present in AI-based malware detection and present three possible directions for future research that consists of combining foundation models in IoT malware code detection, improving the safety of detection models, and combining zero trust architecture in this field.
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视觉目标跟踪是计算机视觉的重要研究方向[1]. 其研究任务是在视频序列首帧中给定任意目标,并在后续视频序列中持续预测目标位置. 目标跟踪被广泛应用于无人驾驶、智能视频监控、人机交互等领域[2]. 如何设计简单、高效的通用视觉目标跟踪方法是一个亟需解决的难题. 尤其在真实复杂场景中,目标表观受光照影响、尺寸变化、严重遮挡等挑战,会产生持续的剧烈变化,从而影响跟踪结果.
近些年,基于卷积神经网络(convolutional neural network, CNN)的目标跟踪获得广泛关注. 然而受限于感受野规模,CNN更多用于处理目标在时间域或空间域的局部特征,未能有效捕获目标特征之间的长期依赖关系[3]. 当前基于CNN的主流跟踪框架主要包括:基于孪生网络(siamese network)[4-7]的目标跟踪和基于在线学习判别式模型[8-10]的目标跟踪. 这些方法在获取图像局部特征时表现优异,但在复杂场景中,如目标被频繁遮挡或出现剧烈形变时,则不能很好地建模特征的全局上下文关系.
此外,基于Transformer的跟踪方案通过引入全局自注意力(self-attention)机制获取特征间长期依赖关系[11-13]. 在此类方案中,多数跟踪方法采用CNN作为主干网络对图像进行特征提取,随后利用Transformer设计编码器和解码器. 文献[14-15]专注简化跟踪步骤,将Transformer作为特征提取器并直接输出预测位置. 然而,此类跟踪方案中起核心作用的自注意力机制,由于其计算复杂度为O(N2),使得其随图像大小增加导致计算量陡增,并直接影响目标跟踪效率.
为了降低计算量,一些研究采用视觉多层感知器(multi-layer perceptron,MLP)来构建主干网络[16-17]. 这些研究使用MLP层代替Transformer中的自注意力层,并在时域进行令牌(token)间信息交互,所谓令牌即目标和搜索区域对应的样本被切分成若干个不重叠的图像块. 令牌间的信息交互进一步简化时域信息的融合操作. MLP的引入降低了计算复杂度,但是在训练和测试中随着令牌数量增加,会显著加大MLP计算量,同样会影响目标跟踪效率.
受全局滤波网络设计启发[3],本文提出利用快速傅里叶变换(fast Fourier transform, FFT)对令牌进行高效融合,以降低视觉MLP模型在令牌数量增加时产生的计算开销. 首先,利用FFT将令牌时域特征转变为频域特征. 随后,在频域空间中捕获目标当前信息及其历史信息与搜索区域信息间的长程依赖关系. 最后,利用快速傅里叶逆变换(inverse FFT, IFFT)将频域特征转换回时域特征. 上述FFT,IFFT运算步骤使得所提跟踪方法,能够以较低的对数复杂度在频域空间快速学习目标在时空维度上的交互关系. 此外,为了更好地自适应目标在跟踪过程中的表观变化,提出一种基于质量评估的目标模板记忆存储机制. 该机制根据当前跟踪状态动态更新记忆存储器中稳定的历史目标信息,用于学习适应目标变化的外观模型,有助于在搜索区域内准确匹配目标.
本文的主要贡献有3点:
1) 提出一种快速获取令牌间长程依赖关系的跟踪算法. 特征提取与融合以端到端的形式进行学习,同时在频域空间以更小的计算复杂度建模目标令牌与搜索区域令牌间的交互关系.
2) 提出一种基于质量评估的目标模板记忆存储机制,动态自适应地捕捉目标在视频序列中的稳定变化过程,提供高质量的长期历史目标信息.
3) 所提跟踪方法在3个公共跟踪数据集LaSOT[18],OTB100[19],UAV123[20]上获得优秀评价.
1. 相关工作
1.1 基于CNN的目标跟踪
目标跟踪框架通常可以被划分为3部分:1)提取图像特征的主干网络;2)目标与搜索区域特征融合模块;3)生成预测位置模块. 多数跟踪方法[5-9]将CNN作为主干网络. 其中,基于孪生网络的跟踪框架以端到端方式进行训练.SiamFC[7]采用全卷积孪生网络提取目标特征,跟踪过程中不进行目标模板更新.DSiam[21]基于孪生网络以正则化线性回归模型动态更新目标模板.SiamRPN[6]利用孪生网络提取目标与搜索区域特征,同时结合目标检测研究中的区域推荐网络对目标位置进行精准定位.SiamRPN++[5]在训练过程中辅以位置均衡策略缓解CNN在训练过程中存在的位置偏见问题. 此外,一些基于在线学习的判别式目标跟踪模型也取得优异性能.DiMP[8]采用端到端网络模型离线学习目标与背景间的差异,同时在线更新目标模板.PrDiMP[9]基于DiMP[8]将概率回归用于端到端训练,在测试阶段对搜索区域生成关于目标状态的条件概率密度来捕获目标. 基于CNN的目标跟踪在训练时容易造成归纳偏置.
1.2 基于Transformer的目标跟踪
当前Transformer网络被广泛用于各项视觉任务中,如目标分类及检测[22-24]. Transformer中的自注意力机制将每个输入元素与其他元素进行相关性计算. 在视觉跟踪研究中,TrDiMP[13]使用Transformer增强目标上下文信息,在编码器中通过自注意力机制增强目标模板特征,利用解码器融合上下文模板进行目标定位. TransT[12]提出一种基于多头注意力机制的特征融合网络,融合后特征分别输入目标分类器及边界回归器. Stark[11]则利用ResNet[25]作为主干网络提取目标特征,以Transformer编码器和解码器进行端到端训练. SwinTrack[14]借鉴Swin Transformer[22],采用完全基于注意力机制的Transformer进行特征提取及融合. ToMP[26]同样是一种完全基于Transformer的跟踪算法,使用一个并行的2阶段跟踪器来回归目标边界. Mixformer[15]提出一种同时混合注意力模块用于特征提取及融合. 基于Transformer的目标跟踪方法虽然取得出色性能,但是随着搜索区域增大,其计算复杂度也将呈2次方增加,从而影响目标跟踪效率.
1.3 基于MLP的混合令牌相关工作
MLP-mixer[16]采用MLP代替Transformer中的自注意力机制进行令牌混合.ResMLP[17]基于MLP-mixer,利用仿射变换代替归一化进行加速处理.gMLP[27]使用空间门控单元在空间维度上给令牌重新加权. 上述MLP混合令牌的研究同样存在计算量增加问题,即随着输入令牌数量增多其时间复杂度会以2次方增加,并且MLP通常存在固定空间权重很难扩展到高分辨率图像的情形.
2. 方法介绍
图1展示了基于FFT的目标与搜索区域间令牌高效混合的目标跟踪框架,该框架为端到端方式. 首先,初始目标模板大小设置为Ht×Wt×3,记忆存储器中存储的历史目标模板的帧数设置为T,搜索目标区域的大小为Hs×Ws×3. 之后,将记忆存储器内所有目标样本和搜索区域对应图像样本,切分成不重叠的、规格为τ×τ×3的图像块,这些图像块称为令牌. 将这些令牌拼接起来,组成1维令牌序列. 该序列包含目标信息与搜索区域信息. 下面分2步进行模型的离线训练.
1) 针对预测目标框分支进行训练. 为了高效学习目标与搜索区域令牌间的长程依赖关系,采用3阶段网络设计进行令牌间混合. 在阶段1中,使用线性嵌入层将原始令牌投影为维度为C的令牌特征,再将这些令牌特征输入至线性嵌入层和包含2个FFT的令牌混合网络层. 在阶段2中,为了扩大模型的感受野,通过线性合并层来减少令牌数量,并将其输出特征维度设置为2C,这一过程由线性合并层和3个FFT的令牌混合网络层组成. 在阶段3中,继续进行线性合并,同时经过6个FFT令牌混合网络层,此时输出的特征维度设置为4C. 将在频域空间中获得的融合令牌信息进行IFFT运算,将频域特征重新转换为时域特征,并输入由3个Conv-BN-ReLU网络组成的预测头网络估计目标位置.
2) 对跟踪质量评估分支进行离线训练,受Stark[11]启发,跟踪质量评估分支由一个3层MLP网络组成,用于评价当前跟踪质量,以决定是否将当前跟踪结果更新到记忆存储器中.
下面将详细介绍基于FFT的令牌混合网络和基于跟踪质量评估的目标模板动态记忆存储机制.
2.1 基于FFT的令牌混合网络
如图1所示,提出的基于FFT令牌混合网络层将特征提取与融合进行集成. 具体地,先利用图像分块操作将原始的2维目标模板和搜索区域样本转化为N个不重叠的τ×τ×3大小的令牌. 经过裁剪等预处理后,得到一组特征矩阵 {{\boldsymbol P}}=\left({\boldsymbol{p}}_{0},{\boldsymbol{p}}_{1},…,{\boldsymbol{p}}_{N-1}\right), {\boldsymbol{p}_{i}}\in {\mathbb{R}}^{3{\tau }^{2}},i\in [0,N-1] . 之后,将 {{\boldsymbol P}} 输入至FFT令牌混合网络,在频域空间快速获得目标特征的多尺度交互及搜索区域与目标之间的有效交互. 其中,FFT令牌融合网络层的结构如图2所示,对于第 i 个令牌先将其映射成 C 维向量:
{\boldsymbol{x}}_{i}={\omega }_{0}{\boldsymbol{p}_{i}}+{\boldsymbol{b}_{0}},i\in \left[0,N-1\right] \text{,} (1) 其中{\boldsymbol{\omega}}_{0}\in {\mathbb{R}}^{3{\tau }^{2}\times C} 为每个令牌首层可学习权重, {\boldsymbol{b}_{0}} 为首层权重位移参数向量, N 为输入令牌个数.
FFT令牌融合网络层的输入特征为 \boldsymbol{X}={(\boldsymbol{x}}_{0},{\boldsymbol{x}}_{1},…, {\boldsymbol{x}}_{N-1})\in {\mathbb{R}}^{C\times N} ,其中 C 为输出通道数. 然后采用式(2)将输入的时域特征转换为频域特征 \boldsymbol{X}' :
{\boldsymbol{X}}'=F\left(\boldsymbol{X}\right)\in {\mathbb{C}}^{H\times W\times N} \text{,} (2) 其中,FFT函数为 F\left(\cdot\right) 用于获得输入特征的频域表达, W 为输入图像的宽, H 为输入图像的高.
FFT令牌混合网络层利用可学习的滤波器 \boldsymbol{K}\in {\mathbb{C}}^{H\times W\times N} 学习 {\boldsymbol{X}}^{'}的频域特征 {\boldsymbol{X}}^{''} :
{\boldsymbol{X}}^{''}=\boldsymbol{K}\odot{\boldsymbol{X}}^{'} \text{,} (3) 其中 \odot 为 \boldsymbol{K} 中每一个元素与 {\boldsymbol{X}}^{'} 对应位置元素间相乘[3].
最后,根据式(4)将频域特征 {\boldsymbol{X}}^{''} 转换为时域特征 {\boldsymbol{X}}^{{*}} ,并更新令牌进入下一层特征融合模块.
{\boldsymbol{X}}^{{*}}={F}^{-1}\left({\boldsymbol{X}}^{''}\right) , (4) 其中 {F}^{-1}\left(\cdot\right) 为IFFT,用于将频域特征转化为时域特征.
参照Stark[11],本文采用一个3层Conv-BN-ReLU预测头网络来估计目标位置. 具体地,估计过程被建模为预测边界框的左上角和右下角坐标的概率值图,并回归概率值图分布获得预测目标的最终坐标. 不同于Stark的预测头网络高度依赖编码器和解码器,本文所提预测头网络由3个简单的全卷积网络组成. 离线训练预测头位置分支的损失 {L}_{\mathrm{l}\mathrm{o}\mathrm{c}} 由 {L}_{1} 损失和 {L}_{\mathrm{g}\mathrm{i}\mathrm{o}\mathrm{u}} 损失组成,具体定义为:
{L}_{\mathrm{l}\mathrm{o}\mathrm{c}}=\alpha {L}_{1}\left({B}_{i},{B}_{\mathrm{p}\mathrm{r}\mathrm{e}\mathrm{d}}\right)+\beta {L}_{\mathrm{g}\mathrm{i}\mathrm{o}\mathrm{u}}\left({B}_{i},{B}_{\mathrm{p}\mathrm{r}\mathrm{e}\mathrm{d}}\right) \text{,} (5) 其中 \alpha 为 {L}_{1} 损失的权重系数,设置 \alpha=5 ; \beta 为 {L}_{\mathrm{g}\mathrm{i}\mathrm{o}\mathrm{u}} 的权重系数,设置 \beta=2 . {B}_{i} 为第 i 帧搜索区域的真实标签, {B}_{\mathrm{p}\mathrm{r}\mathrm{e}\mathrm{d}} 为预测头网络输入预测的目标位置.
2.2 基于跟踪质量评估的目标模板记忆存储机制
为了提升跟踪速度的同时规避跟踪过程中引入的累计误差,多数跟踪算法仅采用第1帧目标模板进行匹配. 然而在跟踪过程中目标表观通常会出现剧烈变化,此时固定目标模板的跟踪方法容易产生漂移. 部分算法采用跟踪响应图的统计特性来预测当前跟踪质量,如使用峰旁比[28]、平均峰值相关能量[29]等. 然而基于上述统计数值判断跟踪质量的做法在经历长期不稳定的跟踪后,容易导致不准确的评分结果.
如果跟踪算法可以及时预先获取当前跟踪质量,并将高质量跟踪结果放入记忆存储器中,则能够有效捕获目标在时序上的稳定表观信息变化,为目标与搜索区域的令牌混合提供有效依据.
因此,在预测头网络中添加了一个用于预测当前跟踪质量的分支. 该分支的输入为令牌融合网络层最终输出的令牌时域特征,输出为2个经过softmax函数处理过后的数值 {S}_{i0} 与 {S}_{i1} . 其中 {S}_{i0} 代表第 i 帧输出的预测目标位置不是目标, {S}_{i1} 表示当前预测结果是目标.当 {S}_{i1}{ > S}_{i0} 时,表示当前跟踪质量良好,可以将当前跟踪结果更新到记忆存储器中,此时设置 {{\epsilon}_{i}}=1 ;当 {S}_{i1}\le {S}_{i0} 时,表示当前跟踪质量较弱,不适宜将跟踪结果更新至记忆存储器,同时设置 {\epsilon}_{i}=0 . {\epsilon}_{i} 表示预测当前跟踪质量评估结果. 离线训练跟踪质量评价分支使用二值交叉熵损失评估,具体定义为:
{L}_{\mathrm{C}\mathrm{E}}={l}_{i}\mathrm{l}\mathrm{b}\left({\epsilon}_{i}\right)+\left(1-{l}_{i}\right)\mathrm{l}\mathrm{b}\left(1-{\epsilon}_{i}\right), (6) 其中 {l}_{i} 为第 i 帧样本真实的标签,当 {l}_{i}=1 时表示当前搜索区域包含真实目标,当 {l}_{i}=0 时表示当前搜索区域不包含搜索目标.
记忆存储器 M 定义为长度 T 的队列,更新间隔设为 {T}_{\mathrm{I}\mathrm{N}\mathrm{R}} . 对应的更新策略如算法1所示,当第 i 帧的质量评估为跟踪状态良好时,即 {\epsilon}_{i}=1 且符合提取间隔,则将当前跟踪结果加入记忆存储队列 M . 若记忆存储队列 M 的长度超过 T ,则选择删除 M 队列中首个元素 {M}_{0} . 当跟踪失败或者跟踪质量较低时,所提基于跟踪质量评估的目标记忆存储机制,能够有效缓解目标模板产生误差带来的消极影响.
该机制的可视化展示如图3所示. 第1帧给定初始目标,并将其存入记忆存储器中. 记忆存储器的长度 T 设置为5,根据跟踪质量评价结果,动态地将可靠的目标模板存入 M 中. 第200帧时,目标被完全遮挡,此时质量评估较差,不进行更新存储操作. 至此, M 中的目标模板分别来自第90帧、第100帧、第110帧、第120帧、第130帧的跟踪结果. 在第260帧时目标重新出现,此时质量评估良好,所以当前 M 存储的目标模板调整为第120帧、第130帧、第240帧、第250帧、第260帧的跟踪结果.
算法1. 基于跟踪质量评估的目标模板记忆存储.
输入: {I}_{0} 为第1帧的目标模板区域,
{I}_{i} 为第 i 帧跟踪结果区域,
{\epsilon}_{i} 为第 i 帧跟踪质量评估结果,
M 为记忆存储器队列,
T 为记忆存储器长度,
{L}_{\mathrm{V}\mathrm{I}\mathrm{D}} 为视频序列长度,
{T}_{\mathrm{I}\mathrm{N}\mathrm{R}} 为更新间隔;
输出: M 为更新后的记忆存储器.
① M=\varnothing ;
② while len( M )< T
③ M\leftarrow M\cup \left\{{I}_{0}\right\};
④ for i=1,2,\cdots {,L}_{\mathrm{V}\mathrm{I}\mathrm{D}}
⑤ if ({\epsilon}_{i} ==1 ) and ( mod (i,{T}_{\mathrm{I}\mathrm{N}\mathrm{R}}) ==0)
⑥ M\leftarrow M\cup \left\{{I}_{i}\right\};
⑦ end if
⑧ if len \left(M\right) < T
⑨ M\leftarrow M\mathrm{r}\mathrm{e}\mathrm{m}\mathrm{o}\mathrm{v}\mathrm{e}{M}_{0};
⑩ end if
⑪ end for
3. 实验结果分析
3.1 模型训练设置
目标模板大小为 {{H}_{\mathrm{t}}\times W}_{\mathrm{t}}\times 3 ,搜索区域的大小为 {{H}_{\mathrm{s}}\times W}_{\mathrm{s}}\times 3 . 设置 {H}_{\mathrm{t}} , {W}_{\mathrm{t}} 两者值均为128; {H}_{\mathrm{s}} , {W}_{\mathrm{s}} 两者值均为384.记忆存储器长度 T=5 . 记忆器更新模板的间隔 {T}_{\mathrm{I}\mathrm{N}\mathrm{R}}=10 . 图像分块操作中块大小 \tau =4 . 训练数据集为LaSOT[18],GOT-10k[30],TrackingNet[31].
考虑到定位和分类的联合学习可能导致2个任务存在次优解 [11]. 因此,借鉴Stark[11]和Mixformer[15]的训练方式,分2步训练特征融合模型. 首先,进行300批次的预测目标位置分支训练,采用Adam[32]损失优化器将学习率设置为 1\mathrm{E}-4 ;其次,进行100批次的预测当前跟踪质量的分支训练,学习率设置为 1\mathrm{E}-5 . 软件环境为Ubuntu20.04,Python3.6,Torch1.10.3,Cuda11.3.硬件环境为NVIDIA RTX3090 24 GB.
3.2 定量分析
在LaSOT[18],OTB100[19],UAV123[20]数据集上验证本文方法的有效性. 评价指标为成功率(success ratio)和精度图(precision plot),其中成功率使用成功率曲线下面积(area under curve, AUC)作为排序依据.
LaSOT[18]数据集包含1400个视频序列,共计70类目标. 其中1120个视频用于训练,280个视频用于测试. 视频序列平均长度2400帧. 数据集包含视野外等14种挑战.图4显示本文算法与TrDiMP[13],TransT[12],Alpha-Refine[33],SiamR-CNN[34],PrDiMP[9],DiMP[8],SiamGAT[35],SiamBAN[36] 8种优秀算法比较结果. 结果表明本文算法在成功率和精度图中均处于领先水平. 精度图方面比TransT高3.3%,成功率比Alpha-Refine高0.8%.图5展示本文算法与5种先进算法在不同挑战下的实验结果,可以看出本文算法在多数挑战中均表现优异.
OTB100[19]数据集包含100个视频序列,涉及快速运动等11种挑战.图6展示本文算法与TransT[12],SiamRPN++[5],SiamBAN[36],PrDiMP[9],DiMP[8],ECO[37],MDNet[38],ATOM[10]的比较结果. 本文方法取得最高的成功率值和精度图值,分别比SiamRPN++ 提升0.2%和0.5%.
UAV123[20]数据集由123个无人机低空拍摄的视频序列构成. 小目标和频繁遮挡是该数据集的独特挑战. 表1显示本文算法与TrDiMP[13],TransT[12], SiamR-CNN[34],SiamGAT[35],SiamBAN[36],PrDiMP[9],DiMP[8],SiamRPN++[5]的比较结果. 本文算法在成功率和精度图评价指标上均排名第一.
表 1 本文算法与其他先进算法在UAV123数据集上的比较Table 1. Comparison of Our Algorithm and Other State-of-the-art Algorithms on UAV123 Dataset算法 AUC 精度图 本文算法 0.702 0.877 TransT 0.691 0.864 PrDiMP 0.690 0.867 TrDiMP 0.680 0.852 DiMP 0.662 0.838 SiamBAN 0.650 0.820 SiamR-CNN 0.649 0.834 SiamGAT 0.646 0.843 SiamRPN++ 0.610 0.803 3.3 定性分析
本节用可视化展示本文算法与6种优秀算法在旋转、快速移动、尺寸变换及遮挡等挑战下的表现.
图7展示LaSOT[18]数据集中bird-17视频序列的跟踪结果. 该视频序列具备快速移动、视野外等挑战. 目标在148~156帧快速向左移动至视野外,导致Alpha-Refine[33]和TrDiMP[13]发生跟踪漂移. 在第184帧中目标再次回归视野内,只有本文算法可以准确跟踪目标. 由于目标同时发生快速移动、运动模糊、旋转等挑战,其他算法均跟踪失败. 而本文算法拥有记忆存储器中的稳定目标模板,可以增强跟踪器对目标表观的自适应能力,并且在搜索目标时可快速计算目标模板和搜索区域之间的匹配关系,因此可以高效、稳健地跟踪目标.
图8展示LaSOT[18]数据集中bicycle-18视频序列的跟踪结果. 在此视频中目标受遮挡、旋转等挑战影响. 第344~400帧目标被岩石遮挡,导致TransT[12]和SiamGAT[35]丢失目标. 第437~517帧目标发生剧烈旋转,SiamGAT,TransT,PrDiMP[9]均无法快速应对剧烈旋转引起的外观突变而发生漂移. 本文算法则依托令牌混合方案快速对目标与搜索区域特征进行交互,有效地获取更加稳健的时空特征,最终成功跟踪目标.
3.4 消融实验
本节验证本文算法中基于FFT的令牌混合网络和基于跟踪质量评估的目标模板动态记忆存储机制的有效性. 表2展示不同变体在LaSOT[18]测试集上的成功率和精度图得分.
表 2 在LaSOT数据集上本文算法的消融实验结果Table 2. Results of the Ablation Experiments of Our Proposed algorithm on LaSOT Dataset模型变体 基于CNN
的融合基于FFT
的融合记忆存
储机制成功率 精度图 平均跟踪
速度/fps变体1 √ 0.648 0.684 23 变体2 √ 0.661 0.709 41 变体3 √ √ 0.667 0.723 34 注:√表示采用的方法. 首先,探讨基于FFT的令牌混合网络的有效性.表2中变体1采用基于CNN融合目标与搜索区域令牌的方法,并且仅利用第1帧初始目标区域作为目标模板. 变体2采用FFT融合方法,同样仅采用第1帧初始目标区域作为目标模板进行匹配. 结果显示,基于FFT的融合方法比基于CNN的融合方法的成功率和精度图分别高1.3%和2.5%. 基于传统CNN的融合方式在训练时只能学习特征间的局部依赖关系,无法获取全局长程依赖,且利用CNN训练模型存在较大的归纳偏置. 为了更加充分融合目标与搜索区域间的信息同时建立两者间的长程依赖关系,本文提出利用FFT进行令牌间的高效融合. 可以观察到在平均跟踪速度上变体2比变体1提升近1倍,结果证实基于FFT令牌混合网络的有效性.
其次,变体3在变体2的基础上增加了基于质量评估的目标模板动态记忆存储机制,用于获得更新稳定的目标模板信息,从而自适应目标表观变化. 由于记忆存储机制增加了目标模板数量,所以对平均跟踪速度上有一定影响. 变体3在测试时的平均跟踪速度比变体2降低了7 fps,但变体3在成功率和精度图上,分别比变体2高出0.6%和1.4%. 结果显示基于跟踪质量评估的目标模板动态记忆存储机制有效.
此外,为了进一步验证本文方法具备高效的特征提取与融合能力. 在LaSOT数据集上将本文方法与基于1阶段训练的Mixformer[15]和基于2阶段训练的TrDiMP[13]进行对比,结果如表3所示. 与采用2阶段训练的TrDiMP[13]相比,本文方法的成功率和精度图分别提升2.7%和5.7%,同时平均跟踪速度比TrDiMP[13]快8 fps.与基于1阶段训练的Mixformer[15]相比,虽然成功率和精度图降低2.5%和2.4%,但是推理速度比Mixformer[15]高9 fps.实验结果表明本文方法在准确率和推理速度间的平衡能力更好,同时34 fps的平均跟踪速度达到跟踪实时性[11]要求(>30 fps).
表 3 LaSOT数据集上推理速度的对比实验结果Table 3. Comparative Experimental Results of Reasoning Speed on LaSOT Dataset方法 出处 成功率 精度图 平均跟踪速度/fps TrDiMP CVPR21 0.640 0.666 26 Mixformer CVPR22 0.692 0.747 25 本文算法 0.667 0.723 34 4. 总 结
本文提出了一种端到端的基于傅里叶变换的高效混合目标与搜索区域令牌的视觉目标跟踪方法. 该方法将特征提取与融合相结合,利用傅里叶变换将令牌的时域特征转换为频域特征,以便快速学习搜索区域与目标模板之间的长程依赖关系. 为了捕获目标在时序上的外观变化,提出了一种基于跟踪质量评估的目标模板动态记忆存储机制,确保更新目标外观模板的合理性. 广泛的实验结果验证了所提方法的有效性.
作者贡献声明:薛万利提出论文整体思路并负责撰写与修改论文;张智彬负责算法设计与实验并撰写论文;裴生雷负责算法设计及论文审核;张开华负责论文修改;陈胜勇参与了论文思路的讨论及审核.
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表 1 开源物联网恶意代码数据集
Table 1 Open Source IoT Malware Datasets
数据类型 数据集 支持的CPU架构 数据收集方式 可执行文件 TWISC[32] MIPS,ARM,PPC,SPARC,X86,X86_64 互联网下载 Badthings[26] MIPS,ARM,PPC,SPARC,SH4 互联网下载 firmware[33] MIPS,ARM,PPC,SPARC,X86,X86_64 互联网下载 IoTPoT[6] MIPS,ARM,PPC,SPARC,X86,X86_64,m68k 部署蜜罐捕获 VirusTotal[34] 多种常见物联网CPU架构 用户自主提交 VirusShare[35] 多种常见物联网CPU架构 部署检测设备捕获 流量数据包 IoT-23[36] 多种常见物联网CPU架构 真实环境捕获 Bot-IoT[37] 多种常见物联网CPU架构 实验室模拟 ToN_IoT[38] 多种常见物联网CPU架构 真实环境捕获 MedBIoT[39] 多种常见物联网CPU架构 真实环境捕获 Kitsune[40] 多种常见物联网CPU架构 实验室模拟 表 2 基于人工智能的跨架构物联网恶意代码检测技术对比
Table 2 Comparison of AI-based Cross-Architecture IoT Malware Detection Techniques
数据来源 特征类别 人工智能算法 数据集 支持的架构 年份 文献[25] 操作码及API SVM,kNN IoTPOT ARM,MIPS,MIPSE 2019 文献[52] 操作码及API SVM,kNN IoTPOT ARM,MIPS,MIPSEL 2018 文献[59] 操作码及CFG SVM firmware ARM,MIPS,PPC,SPARC,X86,X86_64 2019 文献[63] 操作码及ELF文件特征 CNN VirusTotal ARM,MIPS,PPC,SPARC 2020 文献[58] 操作码及IG RNN-CNN IoTPoT,VirusShare ARM,MIPS,PPC 2020 文献[68] 字节序列 SVM VirusTotal ARM,MIPS,PPC,SPARC,X86、X86_64 2020 文献[69] 字节序列 SVM VirusTotal ARM,MIPS,PPC,SPARC,X86,X86_64 2020 文献[70] 字节序列 Bi-GRU-CNN TWISC ARM,MIPS,X86,SuperH4,PPC 2022 文献[50] 可打印字符串 聚类 IoTPOT ARM,MIPS,PPC,SPARC 2018 文献[27] 可打印字符串 RF,kNN,SVM VirusTotal ARM,MIPS,X86,X86-64,PPC,SPARC 2020 文献[71] 图相关特征 RF,kNN,SVM,MLP,LR VirusTotal ARM,MIPS,SPARC,X86,X86-64,PPC 2023 文献[75] 图相关特征 GNN ARM,MIPS,SPARC,PPC,X86-64 2021 文献[72] 系统调用 KMM VirusTotal ARM,Intel X86 2023 表 3 基于人工智能资源限制的物联网恶意代码检测技术对比
Table 3 Comparison of Resource-constrained AI-based IoT Malware Detection Techniques
数据来源 资源限制类别 限制缓解方案 特征选择/处理方法 人工智能算法 年份 文献[79] 计算资源限制 降低特征维度 Word2Vec MLP 2021 文献[80] 计算资源限制 降低特征维度 ACO SVM 2023 文献[81] 计算资源限制 降低特征维度 相关性分析 Random Forest,Bagging,Stacking,SVM,LR,kNN 2021 文献[82] 计算资源限制 降低特征维度 无 SVM,Random Forest,Decision Tree 2023 文献[84] 计算资源限制 降低特征维度 图像化 CNN 2023 文献[33] 计算资源限制 降低算法复杂度 N-gram CFD 2020 文献[85] 计算资源限制 降低算法复杂度 C500-CFG SVM 2019 文献[88] 内存限制 设计轻量级模型 无 LiMNet 2021 文献[87] 内存限制 设计轻量级模型 图像化 LCNN 2021 文献[91] 电力限制 设计可独立于物联网设备部署的检测 图像化 CNN 2021 文献[92] 电力限制 设计可独立于物联网设备部署的检测 过滤电信号噪声 Seq2Seq 2020 文献[93] 电力限制 设计可独立于物联网设备部署的检测 无 SVM 2018 表 4 性能提升的物联网恶意代码检测技术对比
Table 4 Comparison of Performance-enhancing IoT Malware Detection Techniques
数据来源 检测对象 技术重心 人工智能算法 数据集来源 年份 文献[96] 二进制可执行文件 提高检测准确率 CNN+LSTM VirusTotal,VirusShare 2021 文献[99] 二进制可执行文件 提高检测准确率 ResNet18+MobileNetV2+DenseNet161 未公开 2022 文献[98] 二进制可执行文件 提高检测准确率 GCNN 未公开 2023 文献[97] 二进制可执行文件 提高检测准确率 FPT VirusShare 2019 文献[101] 二进制可执行文件 检测混淆恶意代码 SVM 未公开 2020 文献[102] 二进制可执行文件 检测混淆恶意代码 Inception-v3 未公开 2022 文献[103] 二进制可执行文件 检测混淆恶意代码 Bi-LSTM+EfficientNet-B3 VirusTotal 2021 文献[111] 二进制可执行文件 检测混淆恶意代码 Bi-LSTM+SPP-Net 未公开 2022 文献[100] 二进制可执行文件 提高检测模型鲁棒性 VGAE-MalGAN 未公开 2023 文献[119] 流量数据 及时发现攻击行为 Auto-encoder 未公开 2018 文献[15] 流量数据 提高检测模型性能 ANN ToN_IoT 2022 文献[122] 流量数据 提高检测模型性能 RF IoT-23 2022 文献[114] 流量数据 提高检测模型性能 FastGRNN MedBIoT 2020 文献[120] 流量数据 及时发现攻击行为 LR,kNN,RF,AdaBoost 未公开 2019 表 5 挑战与机遇
Table 5 Challenges and Opportunities
类别 具体内容 面临的挑战 缺少针对物联网设备特性的评估指标; 缺少统一可用的动态分析环境; 缺乏对攻击的鲁棒性. 未来研究方向 结合大模型实现物联网恶意代码检测; 提高检测模型安全性; 结合零信任架构实现检测. -
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