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扁平化算法",{"type":18,"tag":26,"props":246,"children":247},{},[248],{"type":24,"value":249},"扁平化算法是伪分支算法的一种扩展形式。如下图所示，原模型由A、B、C、D、E五部分组成，现在我们想保护B、C、D三部分。于是我们可以构建一个switch结构，其中三个分支是原模型的B、C、D，其它分支是伪分支。为了保证B、C、D三部分都能执行一遍，这个switch结构就需要执行多次，所以下图中的'P_loop'到switch节点有一个回转（假设P_loop=True时进行回转，P_loop=False时走到E）。此外，为了保证每次执行switch结构体的时候，都能选择正确的分支，我们还要维护一个指示变量next。如下图所示，最开始的时候，next=P0，我们让P0=3，于是switch就会选择走第3个分支，也就是子图B；然后运行完子图B之后，next的值更新为P3，我们让P3=5，于是switch会选择走第5个分支，也就是子图C；再接着让switch走第1个分支，同时把P_loop的值更新为False。这样就可以按原模型的节点顺序执行，同时攻击者从这个switch结构体看不出来原来的真实节点和执行顺序。要注意的是，P0, P1, ..., P_loop的结果不能让攻击者知道，只有在模型运行时才能确定。",{"type":18,"tag":26,"props":251,"children":252},{},[253],{"type":18,"tag":54,"props":254,"children":256},{"alt":56,"src":255},"https://fileserver.developer.huaweicloud.com/FileServer/getFile/cmtybbs/e64/154/b38/90a1d5d431e64154b387b3660e356ff5.20230330024412.85690607236488918270868730043294:50540329063115:2400:0D74F9E628DC95E4E529AAE9319074A3831F1C576E76EFA2FB7E813DE383079F.png",[],{"type":18,"tag":26,"props":258,"children":259},{},[260],{"type":24,"value":261},"扁平化算法图示",{"type":18,"tag":26,"props":263,"children":264},{},[265],{"type":24,"value":266},"伪分支和扁平化的算法原理很简单，困难的是代码实现：如何在原有的FuncGraph中根据现有的语法规则插入新的节点，保证模型能正确执行，同时推理性能损失最小；模型执行的时候是按节点排列顺序执行的，如何在模型执行过程中，动态调整扁平化算子中next的值和P_loop的值。",{"type":18,"tag":26,"props":268,"children":269},{},[270],{"type":18,"tag":30,"props":271,"children":272},{},[273],{"type":24,"value":274},"3.1.4 混淆结构控制条件设计",{"type":18,"tag":26,"props":276,"children":277},{},[278],{"type":24,"value":279},"上面几个小节提到了，模型子图或节点的保护是通过构造switch结构体来实现的，这一节说明如何设计switch结构的判定条件（针对伪分支算法的）。该判定条件需要满足两个条件：",{"type":18,"tag":26,"props":281,"children":282},{},[283],{"type":24,"value":284},"1、在完成模型混淆后，敌手不能通过观察判定条件推断其结果；",{"type":18,"tag":26,"props":286,"children":287},{},[288],{"type":24,"value":289},"2、对于任意的模型输入（推理时传入的样本），判定条件的返回值恒为True或者恒为False。举个例子，如果某个switch结构体中，真实节点被放在了True分支，伪节点放在了False分支，那么判定条件就需要保证在推理任意一个样本的时候，它返回的都是True，否则的话模型就会使用伪节点去推理，造成推理结果错误。",{"type":18,"tag":26,"props":291,"children":292},{},[293],{"type":24,"value":294},"我们目前设计了基于外部输入的判定模式和用户自定义函数的模式，具体如下。",{"type":18,"tag":26,"props":296,"children":297},{},[298],{"type":18,"tag":30,"props":299,"children":300},{},[301],{"type":24,"value":302},"3.1.4.1基于外部输入的判定模式",{"type":18,"tag":26,"props":304,"children":305},{},[306],{"type":24,"value":307},"在这种模式下，switch节点的结构为：",{"type":18,"tag":26,"props":309,"children":310},{},[311],{"type":18,"tag":54,"props":312,"children":314},{"alt":56,"src":313},"https://fileserver.developer.huaweicloud.com/FileServer/getFile/cmtybbs/e64/154/b38/90a1d5d431e64154b387b3660e356ff5.20230330024436.43917374823114845874556138437172:50540329063115:2400:69D0B722E94AC5C8ECEF776EB9C6C7EEC32B853D10A23A12975B7D598182B9B6.png",[],{"type":18,"tag":26,"props":316,"children":317},{},[318],{"type":24,"value":319},"switch结构体-1",{"type":18,"tag":26,"props":321,"children":322},{},[323],{"type":24,"value":324},"其中y是新增的一个模型输入，在推理时除了输入推理样本，还需要输入y值。我们如下确定Ni的值和真实节点放置的分支：",{"type":18,"tag":26,"props":326,"children":327},{},[328],{"type":24,"value":329},"在混淆模型的时候，用户需要设置随机种子obf_random_seed，并基于函数f(x)生成一个整数branch_control_input，然后我们在构造第i个switch结构的时候，随机生成一个整数Ni，如果branch_control_input>Ni，那么我们把真实节点放在true_branch，否则把真实节点放在false_branch。",{"type":18,"tag":26,"props":331,"children":332},{},[333],{"type":24,"value":334},"这样的话，混淆后的模型里面，判定条件是y>Ni，攻击者即使看到了这个表达式也不知道它的结果是什么。",{"type":18,"tag":26,"props":336,"children":337},{},[338],{"type":24,"value":339},"在加载混淆模型推理的时候，用户只需要输入正确的混淆随机种子，模型就会选择正确的分支执行。",{"type":18,"tag":26,"props":341,"children":342},{},[343],{"type":24,"value":344},"但这样有个问题，比如生成的branch_control_input=10086，而且他的模型只混淆了一个节点，N1=1234。那么其它用户在加载混淆模型推理的时候，生成的branch_control_input=2000，也同样可以使模型选择正确的分支执行。当然，如果混淆节点很多，这种概率是比较小的。",{"type":18,"tag":26,"props":346,"children":347},{},[348],{"type":24,"value":349},"为了解决上述的问题，我们在构造第一个switch节点的时候，使用如下判定方式：",{"type":18,"tag":26,"props":351,"children":352},{},[353],{"type":18,"tag":54,"props":354,"children":356},{"alt":56,"src":355},"https://fileserver.developer.huaweicloud.com/FileServer/getFile/cmtybbs/e64/154/b38/90a1d5d431e64154b387b3660e356ff5.20230330024458.76392952871749191550377654827072:50540329063115:2400:1034F3FF2857F3FCD5AFEDCF522D1846B6EF16A27BCF894D0FF510D4ED2993C7.png",[],{"type":18,"tag":26,"props":358,"children":359},{},[360],{"type":24,"value":361},"switch结构体-2",{"type":18,"tag":26,"props":363,"children":364},{},[365],{"type":24,"value":366},"f(y)是变换函数（参考_get_branch_control_input()函数[2]），R=f(obf_random_seed)，对于y1≠y2，f(y1)等于f(y2)的概率极小。",{"type":18,"tag":26,"props":368,"children":369},{},[370],{"type":24,"value":371},"这样的话，如果其它用户拿到模型后，传入的y≠obf_random_seed的话，得到的结果几乎不可能正确。",{"type":18,"tag":26,"props":373,"children":374},{},[375],{"type":24,"value":376},"上述方法存在一个安全问题，就是攻击者可以看到R=f(obf_random_seed)，由于f(y)目前是公开在Python代码中的，所以攻击者可以通过穷举整数来寻找obf_random_seed。目前obf_random_seed的合法取值范围是0~int64_max（9223372036854775807），假设每秒可以穷举10000次，需要9223372036854775807/(10000x3600x24x365)≈3千万年，这样看来，代价还是很高的。",{"type":18,"tag":26,"props":378,"children":379},{},[380],{"type":18,"tag":381,"props":382,"children":383},"em",{},[384],{"type":24,"value":385},"优化方案：引入权重混淆，也就是在模型算子的权重上面加扰，执行推理时动态解扰。扰动是以 obf_random_seed作为随机种子生成的，如果推理时传入的 obf_random_seed与模型混淆时设置的不同，权重解扰就会错误，从而导致推理结果不正确；从而可以去掉Equal的判断，不会泄露R。",{"type":18,"tag":26,"props":387,"children":388},{},[389],{"type":18,"tag":30,"props":390,"children":391},{},[392],{"type":24,"value":393},"3.1.4.2基于用户自定义函数的判定模式",{"type":18,"tag":26,"props":395,"children":396},{},[397],{"type":24,"value":398},"在这种模式下，switch选择分支的逻辑由用户自己定义。如下图所示，customized_func()是用户自定义的函数，p和q取自节点A输出的Tensor矩阵中的两个元素。customized_func()要满足这个条件：对于任意的输入p和q，customized_func的返回结果恒为True或者恒为False。为什么要满足这个条件？因为对于不同的推理样本，p和q很可能是不同的，为了保证模型能一直选择正确的分支执行，customized_func的结果就必须是恒定的。",{"type":18,"tag":26,"props":400,"children":401},{},[402],{"type":18,"tag":54,"props":403,"children":405},{"alt":56,"src":404},"https://fileserver.developer.huaweicloud.com/FileServer/getFile/cmtybbs/e64/154/b38/90a1d5d431e64154b387b3660e356ff5.20230330024527.41131693018232524661259075043546:50540329063115:2400:9AE63D3585A929455C12FF3A7C607DF37EC9E2138D4A4F3B898B110A4594A926.png",[],{"type":18,"tag":26,"props":407,"children":408},{},[409],{"type":24,"value":410},"switch结构体-3",{"type":18,"tag":26,"props":412,"children":413},{},[414],{"type":24,"value":415},"需要注意的是，模型文件（比如MindSpore IR格式）中只能保存算子名，并不会保存算子的计算逻辑，所以我们在修改模型结构的时候，只能在模型图里面插入算子名'customized_func'，并不会记录函数的主体逻辑（也就是即使攻击者拿到了模型文件，也不知道自定义函数的计算逻辑）。于是在混淆模型的时候，我们调用用户在Python层定义的customized_func来决定把真实节点放在true_branch还是false_branch。加载混淆模型推理的时候，用户同样需要在Python层定义好这个函数，然后框架在推理过程中会调用这个函数的返回结果。",{"type":18,"tag":26,"props":417,"children":418},{},[419],{"type":24,"value":420},"如果仅仅按上面的逻辑设计判定方式，那么攻击者可以这样简单地攻破：定义 attack_func(p, q): return True 或者 attack_func(p, q): return False，然后把attack_func作为 customized_func，一定有一种情形的结果和设置正确的 customized_func得到的推理结果相同。造成这种情形的原因就是每个switch节点在选择分支时，调用 customized_func返回的结果都相同，而不像obf_random_seed模式中，每个switch节点的判定条件都使用了不同的Ni。",{"type":18,"tag":26,"props":422,"children":423},{},[424],{"type":24,"value":425},"所以我们在代码实现的时候，采用了下面这种方式提升安全性：",{"type":18,"tag":26,"props":427,"children":428},{},[429],{"type":24,"value":430},"首先获取用户设置的自定义函数名func_name，并且把函数名的每个字母转成数字保存在func_name_code_中：",{"type":18,"tag":432,"props":433,"children":435},"pre",{"code":434},"func_name_code_.clear();\nstd::transform(func_name.begin(), func_name.end(), std::back_inserter(func_name_code_),\n[](const char &item) { return static_cast(item); });\n",[436],{"type":18,"tag":437,"props":438,"children":439},"code",{"__ignoreMap":7},[440],{"type":24,"value":434},{"type":18,"tag":26,"props":442,"children":443},{},[444],{"type":24,"value":445},"然后第i次调用customized_func的时候，这样确定result的值：",{"type":18,"tag":432,"props":447,"children":449},{"code":448},"int even_num = 2;\nif (func_name_code_[i % func_name_code_.size()] % even_num == 0) {\ni += 1;\nreturn customized_func(p, q);\n}\ni += 1;\nreturn !customized_func(p, q);\n",[450],{"type":18,"tag":437,"props":451,"children":452},{"__ignoreMap":7},[453],{"type":24,"value":448},{"type":18,"tag":26,"props":455,"children":456},{},[457],{"type":24,"value":458},"举个例子：",{"type":18,"tag":26,"props":460,"children":461},{},[462],{"type":24,"value":463},"假如用户在混淆模型的时候，设置的func_name是'my_func'，包含7个字符，我们把它转成数字列表：[109, 121, 95, 102, 117, 110, 99]。",{"type":18,"tag":26,"props":465,"children":466},{},[467],{"type":24,"value":468},"在创建第1个switch节点的时候，i=0，func_name_code_[i % func_name_code_.size()=109，109为奇数，所以result=!customized_func(p, q)；",{"type":18,"tag":26,"props":470,"children":471},{},[472],{"type":24,"value":473},"在创建第2个switch节点的时候，121为奇数，所以result=!customized_func(p, q)；",{"type":18,"tag":26,"props":475,"children":476},{},[477],{"type":24,"value":478},"...",{"type":18,"tag":26,"props":480,"children":481},{},[482],{"type":24,"value":483},"在创建第6个switch节点的时候，110为偶数，所以result=customized_func(p, q)；",{"type":18,"tag":26,"props":485,"children":486},{},[487],{"type":24,"value":488},"在创建第7个switch节点的时候，99为奇数，所以result=!customized_func(p, q)；",{"type":18,"tag":26,"props":490,"children":491},{},[492],{"type":24,"value":493},"在创建第8个switch节点的时候，109为奇数，所以result=!customized_func(p, q)；",{"type":18,"tag":26,"props":495,"children":496},{},[497],{"type":24,"value":498},"在创建第9个switch节点的时候，121为奇数，所以result=!customized_func(p, q)；",{"type":18,"tag":26,"props":500,"children":501},{},[502],{"type":24,"value":478},{"type":18,"tag":26,"props":504,"children":505},{},[506],{"type":24,"value":507},"也就是说，result的值不仅和customized_func()的值有关，还和函数名以及调用次序i有关（所以在加载混淆模型进行推理的时候，i要复位为0）。这样的话，除非攻击者知道原来的自定义函数名，否则是不能得到正确结果的。",{"type":18,"tag":26,"props":509,"children":510},{},[511],{"type":24,"value":512},"需要注意的是，由于result的值和调用customized_func()的次序有关，所以在模型混淆的处理过程中和加载混淆后模型进行推理时，被保护节点的处理顺序和推理执行先后顺序要一致，否则会导致错误。举个例子，假如在混淆的时候，先后对A、B、C三个节点进行了保护，也就是构造了3个switch结构、调用了三次customized_func()，假设这3次调用得到的result值分别是True、False、True，那么A、B、C分别会被放在switch的true_branch、false_branch、true_branch中。完成混淆后，加载混淆模型进行推理，由于要执行3个switch结构体，所以也要调用3次 customized_func，对应结果同样是True、False、True，如果在这个过程中，A、B、C的执行顺序是B、A、C，那么在执行含有B的switch结构体的时候，会选择true_branch中的节点，但实际上在混淆的时候，B是放false_branch中的，所以会导致推理结果错误。",{"type":18,"tag":26,"props":514,"children":515},{},[516],{"type":24,"value":517},"为了避免上述错误，我们在遍历图的节点做混淆的时候，会先调用sorted_nodes = TopoSort(node)接口，把获取到的节点的顺序排列成模型推理时的节点执行顺序。",{"type":18,"tag":26,"props":519,"children":520},{},[521],{"type":18,"tag":30,"props":522,"children":523},{},[524],{"type":24,"value":525},"3.1.5 全图混淆遍历算法（仅对于3.1.2中的方案一）",{"type":18,"tag":26,"props":527,"children":528},{},[529],{"type":24,"value":530},"以上只是讲述了对于一个节点或者一部分子图进行混淆的方式，那么对于一个完整的AI模型，我们如何去选取混淆的部分？进行什么方式的混淆呢？这节将给出答案。",{"type":18,"tag":26,"props":532,"children":533},{},[534],{"type":24,"value":535},"首先，对于目前已经支持混淆的算子，我们内置了一份算子白名单。先使用sorted_nodes.reverse()逆向遍历模型的所有算子节点（也就是从模型的最下层往上遍历），当遇到一个可混淆算子时，我们把该算子和算子名称分别存储在字典node_dict_和列表node_names_中，并且向上查找他的父节点，如果父节点也为可混淆算子，则存储该父节点并持续向上查找，直到父节点为不可混淆算子，我们将这个不可混淆的父节点的名称存储在另一个列表parent_names中，该节点即为我们添加switch算子和伪分支的地方。这样操作会让我们对任意输入模型生成一个混淆目标算子名称列表，一个父节点名称列表和一个节点字典，如下图所示：",{"type":18,"tag":26,"props":537,"children":538},{},[539],{"type":18,"tag":54,"props":540,"children":542},{"alt":56,"src":541},"https://fileserver.developer.huaweicloud.com/FileServer/getFile/cmtybbs/e64/154/b38/90a1d5d431e64154b387b3660e356ff5.20230330024631.11391546051310009146574695272189:50540329063115:2400:C31939A8C478898F7E3BBC5FC41BF59B21EBB7DCD01D547938F5E345093D265D.png",[],{"type":18,"tag":26,"props":544,"children":545},{},[546],{"type":24,"value":547},"遍历算法图示",{"type":18,"tag":26,"props":549,"children":550},{},[551],{"type":24,"value":552},"这样的储存可以大大提升后续查找算子的效率。后续在做子图伪分支混淆时，可直接使用混淆目标列表中的节点生成伪分支中的算子节点，并将switch算子、原始分支和伪分支添加到对应的父节点位置上。在存储可混淆节点时，我们添加了随机性，即有一定概率不混淆该算子，保证了即使模型相同，每次混淆后的结果也不一样，提高了安全性。",{"type":18,"tag":26,"props":554,"children":555},{},[556],{"type":18,"tag":30,"props":557,"children":558},{},[559],{"type":24,"value":560},"3.1.6 权重混淆算法",{"type":18,"tag":26,"props":562,"children":563},{},[564],{"type":24,"value":565},"3.1.4.1节提到了使用权重混淆可以提升结构混淆的安全性。另外，使用权重混淆也可以保护模型的权重信息。",{"type":18,"tag":26,"props":567,"children":568},{},[569],{"type":24,"value":570},"权重混淆的思路也很简单。参考论文[3]中的乘法加扰的方式，在混淆模型时，把线性算子（卷积算子、全连接算子等）的权重乘以一个系数μ（基于混淆随机种子生成），同时在该算子后面插入一个乘法算子，乘法算子的一个输入是原线性算子的输出，另一个输入来自模型推理时的额外输入。以卷积算子为例，混淆后的计算图如下所示：",{"type":18,"tag":26,"props":572,"children":573},{},[574],{"type":18,"tag":54,"props":575,"children":577},{"alt":56,"src":576},"https://fileserver.developer.huaweicloud.com/FileServer/getFile/cmtybbs/e64/154/b38/90a1d5d431e64154b387b3660e356ff5.20230330024658.97390634101134793675230361422654:50540329063115:2400:9C59B3F7789C7D39DE48AEAF5E86DB6A1FD6F9EC5ED8CD7C94A7012E12530ACD.png",[],{"type":18,"tag":26,"props":579,"children":580},{},[581],{"type":24,"value":582},"权重混淆图示",{"type":18,"tag":26,"props":584,"children":585},{},[586,591,593],{"type":18,"tag":30,"props":587,"children":588},{},[589],{"type":24,"value":590},"3.2",{"type":24,"value":592}," ",{"type":18,"tag":30,"props":594,"children":595},{},[596],{"type":24,"value":597},"代码实现设计",{"type":18,"tag":26,"props":599,"children":600},{},[601],{"type":18,"tag":30,"props":602,"children":603},{},[604],{"type":24,"value":605},"3.2.1 流程概览",{"type":18,"tag":26,"props":607,"children":608},{},[609],{"type":24,"value":610},"模型动态混淆目前已基于昇思MindSpore进行了实现[4]，实现流程图如下所示：",{"type":18,"tag":26,"props":612,"children":613},{},[614],{"type":18,"tag":54,"props":615,"children":617},{"alt":56,"src":616},"https://fileserver.developer.huaweicloud.com/FileServer/getFile/cmtybbs/e64/154/b38/90a1d5d431e64154b387b3660e356ff5.20230330024718.89194553745401631728938628506928:50540329063115:2400:176CE71300BFFFC727001FE314BA0003F8EF73FFD25DB1CD1DC245C91AB355BA.png",[],{"type":18,"tag":26,"props":619,"children":620},{},[621],{"type":24,"value":622},"模型混淆代码流程",{"type":18,"tag":26,"props":624,"children":625},{},[626],{"type":24,"value":627},"上图为动态混淆过程，对于一个待混淆的MindSpore IR格式的模型，先使用load函数将其转为FuncGraph格式，然后基于动态混淆算法（伪分支、扁平化、权重混淆）对ANF图进行修改，在修改过程中，要使用控制流算子Switch创建混淆分支，且在插入控制流算子时，使用用户设置的控制流函数或者混淆种子作为分支选择依据。混淆结束后，会输出一个混淆之后的MindSpore IR模型，敌手此时若窃取到了混淆后的模型，也很难知道原模型的真实结构，并且在不知道混淆种子或者用户的自定义函数的情况下，不能使用模型进行正确的推理。",{"type":18,"tag":26,"props":629,"children":630},{},[631],{"type":18,"tag":54,"props":632,"children":634},{"alt":56,"src":633},"https://fileserver.developer.huaweicloud.com/FileServer/getFile/cmtybbs/e64/154/b38/90a1d5d431e64154b387b3660e356ff5.20230330024742.31376745627095541812621464590288:50540329063115:2400:467C05EC229C498289026C41FC3718B5DB2C5AF01230174A75C0DC43A57A8C47.png",[],{"type":18,"tag":26,"props":636,"children":637},{},[638],{"type":24,"value":639},"混淆模型推理流程",{"type":18,"tag":26,"props":641,"children":642},{},[643],{"type":24,"value":644},"上图为使用混淆模型的过程。和正常MindSpore IR模型的导入、推理过程（network=nn.GraphCell(load(model.mindir))）相比，导入混淆模型主要有以下不同：",{"type":18,"tag":26,"props":646,"children":647},{},[648],{"type":24,"value":649},"1、若用户在混淆模型阶段设置了自定义函数，则在导入混淆模型的时候，需要在load()接口中传入之前定义的函数（Python函数）；",{"type":18,"tag":26,"props":651,"children":652},{},[653],{"type":24,"value":654},"2、若用户在混淆模型阶段设置了混淆种子，则在导入混淆模型的时候，需要在nn.GraphCell()函数中传入混淆种子。",{"type":18,"tag":26,"props":656,"children":657},{},[658],{"type":18,"tag":30,"props":659,"children":660},{},[661],{"type":24,"value":662},"3.2.2 实现细节",{"type":18,"tag":26,"props":664,"children":665},{},[666],{"type":24,"value":667},"模型动态混淆提供了export()、obfuscate_model()、load()等Python接口供用户调用，内部实现则是基于C++接口做的。具体新增和改动的接口、文件如下",{"type":18,"tag":26,"props":669,"children":670},{},[671],{"type":24,"value":672},"1 mindspore/python/mindspore/train/serialization.py里增加了obfuscate_model()函数，作为对外接口；",{"type":18,"tag":26,"props":674,"children":675},{},[676,678],{"type":24,"value":677},"2 mindspore/ccsrc/pipeline/jit/",{"type":18,"tag":679,"props":680,"children":684},"a",{"href":681,"rel":682},"http://pipeline.cc%E5%A2%9E%E5%8A%A0%E4%BA%86%E5%8A%A8%E6%80%81%E6%B7%B7%E6%B7%86%E4%B8%BB%E5%87%BD%E6%95%B0DynamicObfuscateMindIR()%EF%BC%9B",[683],"nofollow",[685],{"type":24,"value":686},"http://pipeline.cc增加了动态混淆主函数DynamicObfuscateMindIR()；",{"type":18,"tag":26,"props":688,"children":689},{},[690,692],{"type":24,"value":691},"3 mindspore/ccsrc/utils/下面加了dynamic_obfuscation目录，放置动态混淆相关的文件，其中包括动态混淆核心逻辑代码",{"type":18,"tag":679,"props":693,"children":696},{"href":694,"rel":695},"http://dynamic%5C_obfuscation.cc%E5%92%8C%E7%94%A8%E6%88%B7%E8%87%AA%E5%AE%9A%E4%B9%89%E5%87%BD%E6%95%B0%E6%B3%A8%E5%86%8C%E6%96%87%E4%BB%B6http://registry%5C_opaque%5C_predicate.cc%EF%BC%9B",[683],[697],{"type":24,"value":698},"http://dynamic\\_obfuscation.cc和用户自定义函数注册文件http://registry\\_opaque\\_predicate.cc；",{"type":18,"tag":26,"props":700,"children":701},{},[702,704],{"type":24,"value":703},"4 mindspore/core/ops/目录下增加了",{"type":18,"tag":679,"props":705,"children":708},{"href":706,"rel":707},"http://opaquePredicate.cc%EF%BC%8C%E4%BD%9C%E4%B8%BA%E7%94%A8%E6%88%B7%E8%87%AA%E5%AE%9A%E4%B9%89%E5%87%BD%E6%95%B0%E7%AE%97%E5%AD%90%E7%9A%84%E5%90%8E%E7%AB%AF%E5%8E%9F%E8%AF%AD%EF%BC%8C%E5%81%9A%E6%B7%B7%E6%B7%86%E7%9A%84%E6%97%B6%E5%80%99%E9%9C%80%E8%A6%81%E5%9C%A8%E5%8E%9F%E6%A8%A1%E5%9E%8B%E7%9A%84FuncGraph%E4%B8%AD%E6%8F%92%E5%85%A5%E8%AF%A5%E7%AE%97%E5%AD%90%EF%BC%9B",[683],[709],{"type":24,"value":710},"http://opaquePredicate.cc，作为用户自定义函数算子的后端原语，做混淆的时候需要在原模型的FuncGraph中插入该算子；",{"type":18,"tag":26,"props":712,"children":713},{},[714,716],{"type":24,"value":715},"5 mindspore/ccsrc/plugin/device/cpu/kernel/增加",{"type":18,"tag":679,"props":717,"children":720},{"href":718,"rel":719},"http://opaque%5C_predicate%5C_kernel.cc%EF%BC%8C%E5%AF%B9%E5%BA%94%E4%BA%8Ehttp://opaquePredicate.cc%E7%9A%84CPU%E5%90%8E%E7%AB%AF%E5%AE%9E%E7%8E%B0%E3%80%82",[683],[721],{"type":24,"value":722},"http://opaque\\_predicate\\_kernel.cc，对应于http://opaquePredicate.cc的CPU后端实现。",{"type":18,"tag":26,"props":724,"children":725},{},[726],{"type":18,"tag":54,"props":727,"children":729},{"alt":56,"src":728},"https://fileserver.developer.huaweicloud.com/FileServer/getFile/cmtybbs/e64/154/b38/90a1d5d431e64154b387b3660e356ff5.20230330024814.12144512498604203242065505356449:50540329063115:2400:55A53E177893CFA39E56F503A732AA55A1EB90EEA79694478F7D91FAF6FCE648.png",[],{"type":18,"tag":26,"props":731,"children":732},{},[733],{"type":24,"value":734},"接口调用关系图",{"type":18,"tag":26,"props":736,"children":737},{},[738],{"type":24,"value":739},"上图是内部接口依赖关系图，灰色模块代表Python接口，蓝色模块代表C++接口。",{"type":18,"tag":26,"props":741,"children":742},{},[743,745],{"type":24,"value":744},"混淆逻辑的实现是在C++中做的，而提供给用户使用的接口是Python接口，他们之间通过mindspore/ccsrc/pipeline/jit/",{"type":18,"tag":679,"props":746,"children":749},{"href":747,"rel":748},"http://init.cc%5C%5B6%5C%5D%E4%B8%AD%E7%9A%84%E6%8E%A5%E5%8F%A3%E5%AE%9A%E4%B9%89%E8%BF%9E%E6%8E%A5%EF%BC%9A",[683],[750],{"type":24,"value":751},"http://init.cc\\[6\\]中的接口定义连接：",{"type":18,"tag":432,"props":753,"children":755},{"code":754},"(void)m.def(\"dynamic_obfuscate_mindir\", &mindspore::pipeline::DynamicObfuscateMindIR, py::arg(\"file_name\"),\npy::arg(\"obf_ratio\"), py::arg(\"branch_control_input\") = py::int_(0), py::arg(\"dec_key\") = nullptr,\npy::arg(\"key_len\") = py::int_(0), py::arg(\"dec_mode\") = py::str(\"AES-GCM\"),\n\"Obfuscate a mindir model by dynamic obfuscation.\");\n",[756],{"type":18,"tag":437,"props":757,"children":758},{"__ignoreMap":7},[759],{"type":24,"value":754},{"type":18,"tag":26,"props":761,"children":762},{},[763,765],{"type":24,"value":764},"在customized_func模式下，用户传入的Python函数先由add_opaque_predicate()保存，然后由",{"type":18,"tag":679,"props":766,"children":769},{"href":767,"rel":768},"http://registry%5C_opaque%5C_predicate.cc%E4%BF%9D%E5%AD%98%E5%9C%A8C++%E7%8E%AF%E5%A2%83%E4%B8%AD%E3%80%82%E5%9C%A8%E6%A8%A1%E5%9E%8B%E6%B7%B7%E6%B7%86%E8%A6%81%E7%94%A8%E5%88%B0%E8%87%AA%E5%AE%9A%E4%B9%89%E5%87%BD%E6%95%B0%E9%80%BB%E8%BE%91%E7%9A%84%E6%97%B6%E5%80%99%EF%BC%8C%E9%80%9A%E8%BF%87OpaquePredicateInfer()%E5%92%8COpaquePredicateKernelMod::LaunchKernel()%E5%8E%BB%E9%97%B4%E6%8E%A5%E8%B0%83%E7%94%A8%E3%80%82%E8%A6%81%E6%B3%A8%E5%86%8C%E8%87%AA%E5%AE%9A%E4%B9%89%E5%87%BD%E6%95%B0%E7%AE%97%E5%AD%90%E7%9A%84%E5%8E%9F%E5%9B%A0%E6%98%AF%E7%AE%97%E5%AD%90%E6%98%AF%E6%A8%A1%E5%9E%8B%E7%9A%84%E5%9F%BA%E6%9C%AC%E7%BB%84%E6%88%90%E5%8D%95%E4%BD%8D%EF%BC%8C%E8%A6%81%E6%83%B3%E5%9C%A8%E6%A8%A1%E5%9E%8B%E6%89%A7%E8%A1%8C%E6%8E%A8%E7%90%86%E7%9A%84%E6%97%B6%E5%80%99%E8%B0%83%E7%94%A8%E7%94%A8%E6%88%B7%E8%87%AA%E5%AE%9A%E4%B9%89%E7%9A%84%E5%87%BD%E6%95%B0%E9%80%BB%E8%BE%91%EF%BC%8C%E5%B0%B1%E9%9C%80%E8%A6%81%E6%8A%8A%E8%87%AA%E5%AE%9A%E4%B9%89%E5%87%BD%E6%95%B0%E7%9A%84%E8%B0%83%E7%94%A8%E5%B0%81%E8%A3%85%E6%88%90%E7%AE%97%E5%AD%90%E7%9A%84%E5%BD%A2%E5%BC%8F%E3%80%82",[683],[770],{"type":24,"value":771},"http://registry\\_opaque\\_predicate.cc保存在C++环境中。在模型混淆要用到自定义函数逻辑的时候，通过OpaquePredicateInfer()和OpaquePredicateKernelMod::LaunchKernel()去间接调用。要注册自定义函数算子的原因是算子是模型的基本组成单位，要想在模型执行推理的时候调用用户自定义的函数逻辑，就需要把自定义函数的调用封装成算子的形式。",{"type":18,"tag":773,"props":774,"children":776},"h3",{"id":775},"_33-使用场景",[777],{"type":18,"tag":30,"props":778,"children":779},{},[780],{"type":24,"value":781},"3.3 使用场景",{"type":18,"tag":26,"props":783,"children":784},{},[785],{"type":24,"value":786},"模型动态混淆是基于MindSpore IR语法实现的（也就是对FuncGraph对象进行混淆处理），目前支持以下两种场景：",{"type":18,"tag":26,"props":788,"children":789},{},[790],{"type":24,"value":791},"① 用户已经训练好了一个nn.Cell类的network，在使用export() 接口导出MindSpore IR模型时，可以传入动态混淆参数，得到一个混淆后的MindSpore IR模型；",{"type":18,"tag":26,"props":793,"children":794},{},[795],{"type":24,"value":796},"② 用户有一个未混淆的MindSpore IR模型，想对其进行混淆，可以采用新增的Python接口obfuscate_model() 进行混淆。",{"type":18,"tag":26,"props":798,"children":799},{},[800],{"type":24,"value":801},"使用样例（官网教程链接[7]）：",{"type":18,"tag":26,"props":803,"children":804},{},[805],{"type":24,"value":806},"该特性提供的基本功能包括混淆模型和导入混淆后的模型，通过Python接口供用户调用，示例：",{"type":18,"tag":26,"props":808,"children":809},{},[810],{"type":18,"tag":30,"props":811,"children":812},{},[813],{"type":24,"value":814},"混淆模型：",{"type":18,"tag":26,"props":816,"children":817},{},[818],{"type":24,"value":819},"场景1，在export mindir时进行混淆：",{"type":18,"tag":432,"props":821,"children":823},{"code":822},"from mindspore import export\nobf_config = {'obf_ratio': 0.01, 'obf_random_seed': 1, 'customized_func':f1}\nexport(net, input, file_name=\"obf_net\", file_format=\"MINDIR\", obf_config=obf_config)\n",[824],{"type":18,"tag":437,"props":825,"children":826},{"__ignoreMap":7},[827],{"type":24,"value":822},{"type":18,"tag":26,"props":829,"children":830},{},[831],{"type":24,"value":832},"上面代码的功能是把nn.Cell类的net导出成一个混淆后的mindir文件。",{"type":18,"tag":26,"props":834,"children":835},{},[836],{"type":24,"value":837},"场景2，把一个已有的MindSpore IR模型进行混淆：",{"type":18,"tag":432,"props":839,"children":841},{"code":840},"from mindspore import obfuscate_model\nobf_config = {'original_model_path': \"xxx.mindir\",\n'save_model_path': \"./obf_net\", 'model_inputs': [input, ],\n'obf_ratio': 0.01, 'obf_random_seed': 1, 'customized_func':f1}\nobfuscate_model(obf_config)\n",[842],{"type":18,"tag":437,"props":843,"children":844},{"__ignoreMap":7},[845],{"type":24,"value":840},{"type":18,"tag":26,"props":847,"children":848},{},[849],{"type":24,"value":850},"上面代码的功能是把MindSpore IR模型xxx.mindir进行混淆，输出成混淆后的MindSpore IR模型obf_net.mindir。",{"type":18,"tag":26,"props":852,"children":853},{},[854],{"type":18,"tag":30,"props":855,"children":856},{},[857],{"type":24,"value":858},"运行混淆后的模型：",{"type":18,"tag":26,"props":860,"children":861},{},[862],{"type":24,"value":863},"1、如果用户在混淆模型的阶段设置了'customized_func'，那么可以如下加载推理混淆后的模型：",{"type":18,"tag":432,"props":865,"children":867},{"code":866},"from mindspore import load\nimport mindspore.nn as nn\ncustom_ops=f1\nobf_graph = load(\"obf_net.mindir\", obf_func=custom_ops)\nobf_net = nn.GraphCell(obf_graph )\nprint(obf_net(input).asnumpy())\n",[868],{"type":18,"tag":437,"props":869,"children":870},{"__ignoreMap":7},[871],{"type":24,"value":866},{"type":18,"tag":26,"props":873,"children":874},{},[875],{"type":24,"value":876},"2、如果用户在混淆模型的阶段设置了'obf_random_seed'，那么可以如下加载推理混淆后的模型：",{"type":18,"tag":432,"props":878,"children":880},{"code":879},"from mindspore import load\nimport mindspore.nn as nn\nobf_graph = load(\"obf_net.mindir\")\nobf_net = nn.GraphCell(obf_graph, obf_random_seed=xx)\nprint(obf_net(input).asnumpy())\n",[881],{"type":18,"tag":437,"props":882,"children":883},{"__ignoreMap":7},[884],{"type":24,"value":879},{"type":18,"tag":26,"props":886,"children":887},{},[888],{"type":18,"tag":30,"props":889,"children":890},{},[891],{"type":24,"value":892},"04",{"type":18,"tag":26,"props":894,"children":895},{},[896],{"type":18,"tag":30,"props":897,"children":898},{},[899],{"type":24,"value":900},"加入我们一起讨论",{"type":18,"tag":26,"props":902,"children":903},{},[904],{"type":24,"value":905},"如果您对模型动态混淆感兴趣，或者有新的想法，欢迎加入Trusted AI SIG[8]，和我们一起探讨！（添加MindSpore小助手微信 'mindspore0328'，发送“加入Trusted AI SIG”，就可以加入我们SIG微信群噢！）",{"type":18,"tag":26,"props":907,"children":908},{},[909],{"type":24,"value":910},"扫码添加MindSpore小助手",{"type":18,"tag":26,"props":912,"children":913},{},[914],{"type":18,"tag":54,"props":915,"children":917},{"alt":56,"src":916},"https://fileserver.developer.huaweicloud.com/FileServer/getFile/cmtybbs/e64/154/b38/90a1d5d431e64154b387b3660e356ff5.20230330025010.82804109968760390589438697567286:50540329063115:2400:F5BEE5740DE3D2171EDEB4767D6BF9754DBBC5B30BB0A4410F7FA058407FF9F6.png",[],{"type":18,"tag":26,"props":919,"children":920},{},[921],{"type":18,"tag":30,"props":922,"children":923},{},[924],{"type":24,"value":925},"参考链接",{"type":18,"tag":26,"props":927,"children":928},{},[929,931],{"type":24,"value":930},"[1]",{"type":18,"tag":679,"props":932,"children":935},{"href":933,"rel":934},"https://zhuanlan.zhihu.com/p/591593493",[683],[936],{"type":24,"value":933},{"type":18,"tag":26,"props":938,"children":939},{},[940,942],{"type":24,"value":941},"[2]",{"type":18,"tag":679,"props":943,"children":946},{"href":944,"rel":945},"https://gitee.com/mindspore/mindspore/blob/r2.0/mindspore/python/mindspore/common/api.py",[683],[947],{"type":24,"value":944},{"type":18,"tag":26,"props":949,"children":950},{},[951,953],{"type":24,"value":952},"[3]",{"type":18,"tag":679,"props":954,"children":957},{"href":955,"rel":956},"https://www.usenix.org/system/files/atc22-shen.pdf",[683],[958],{"type":24,"value":955},{"type":18,"tag":26,"props":960,"children":961},{},[962,964],{"type":24,"value":963},"[4]",{"type":18,"tag":679,"props":965,"children":968},{"href":966,"rel":967},"https://gitee.com/mindspore/mindspore/tree/r2.0/mindspore/ccsrc/utils/dynamic%5C_obfuscation",[683],[969],{"type":24,"value":970},"https://gitee.com/mindspore/mindspore/tree/r2.0/mindspore/ccsrc/utils/dynamic\\_obfuscation",{"type":18,"tag":26,"props":972,"children":973},{},[974,976],{"type":24,"value":975},"[5]",{"type":18,"tag":679,"props":977,"children":980},{"href":978,"rel":979},"http://findresultsonline.com/?dn=pipeline.cc&rg=2686291&%5C_slsen=0&pid=9PO7FO5YW",[683],[981],{"type":24,"value":982},"http://findresultsonline.com/?dn=pipeline.cc&rg=2686291&\\_slsen=0&pid=9PO7FO5YW",{"type":18,"tag":26,"props":984,"children":985},{},[986,988],{"type":24,"value":987},"[6]",{"type":18,"tag":679,"props":989,"children":992},{"href":990,"rel":991},"https://init.cc/",[683],[993],{"type":24,"value":990},{"type":18,"tag":26,"props":995,"children":996},{},[997,999],{"type":24,"value":998},"[7]",{"type":18,"tag":679,"props":1000,"children":1003},{"href":1001,"rel":1002},"https://mindspore.cn/mindarmour/docs/zh-CN/master/dynamic%5C_obfuscation%5C_protection.html",[683],[1004],{"type":24,"value":1005},"https://mindspore.cn/mindarmour/docs/zh-CN/master/dynamic\\_obfuscation\\_protection.html",{"type":18,"tag":26,"props":1007,"children":1008},{},[1009,1011],{"type":24,"value":1010},"[8]",{"type":18,"tag":679,"props":1012,"children":1015},{"href":1013,"rel":1014},"https://mindspore.cn/community/SIG/detail/?name=Trusted%20AI%20SIG",[683],[1016],{"type":24,"value":1013},{"title":7,"searchDepth":1018,"depth":1018,"links":1019},4,[1020],{"id":775,"depth":1021,"text":781},3,"markdown","content:technology-blogs:zh:2214.md","content","technology-blogs/zh/2214.md","technology-blogs/zh/2214","md",1776506121043]