我的问题是如何检查密码是否已经使用Bcrypt加密。如果是，请让Bcrypt不做任何事情，只需保留密码就好。我正在使用Java（EE），Spring。 / p>
public static String hashPassword(String userPassword) {
String bCrypt = null;
if (userPassword == null)
throw new NullPointerException("Input password for hashing was null.");
bCrypt = BCrypt.hashpw(userPassword, BCrypt.gensalt(12));
return bCrypt;
}
答案 0 :(得分：2)
没有办法检查是否已经应用了bcrypt算法，因为一个CAN会创建一个密码重合一个bcrypt的密码，但是你可以使用一个技巧：让密码不允许超过40个字符，然后检查如果密码大于59个字符（或根据您的逻辑调整得更好），那么您可以推断出密码是否加密。
答案 1 :(得分：1)
通常，您不应该只将具有任何值的字符串替换为具有另一个任何值的字符串。问题是有人可能输入的密码与你的bcrypt输出格式相同，你无法区分这两种密码。
幸运的是，你的 bcrypt函数的输出是一个字符串，它包含一个版本，“salt rounds”，salt和hash，由$个分隔符字符包围。因此，区分二者的最佳方法是编写一个与以下输出匹配的正则表达式（来自jBCrypt 0.3源代码）：
rs.append("$2");
if (minor >= 'a')
rs.append(minor);
rs.append("$");
if (rounds < 10)
rs.append("0");
rs.append(Integer.toString(rounds));
rs.append("$");
rs.append(encode_base64(saltb, saltb.length));
rs.append(encode_base64(hashed, bf_crypt_ciphertext.length * 4 - 1));
return rs.toString();
但是我建议你在数据库中添加一个列，指示行上使用的哈希算法（如果有的话）。
答案 2 :(得分：0)
要了解编码后的密码是否确实像BCrypt，请尝试以下操作
Deeplearning4j OOM Exception Encountered for ComputationGraph
Timestamp: 2019-01-12 14:59:32.940
Thread ID 1
Thread Name main
Stack Trace:
java.lang.OutOfMemoryError: Failed to allocate memory within limits: totalBytes (470M + 7629M) > maxBytes (7851M)
at org.bytedeco.javacpp.Pointer.deallocator(Pointer.java:580)
at org.deeplearning4j.nn.layers.BaseCudnnHelper$DataCache.<init>(BaseCudnnHelper.java:119)
at org.deeplearning4j.nn.layers.recurrent.CudnnLSTMHelper.activate(CudnnLSTMHelper.java:509)
at org.deeplearning4j.nn.layers.recurrent.LSTMHelpers.activateHelper(LSTMHelpers.java:205)
at org.deeplearning4j.nn.layers.recurrent.LSTM.activateHelper(LSTM.java:163)
at org.deeplearning4j.nn.layers.recurrent.LSTM.activate(LSTM.java:140)
at org.deeplearning4j.nn.graph.vertex.impl.LayerVertex.doForward(LayerVertex.java:110)
at org.deeplearning4j.nn.graph.ComputationGraph.outputOfLayersDetached(ComputationGraph.java:2316)
at org.deeplearning4j.nn.graph.ComputationGraph.output(ComputationGraph.java:1727)
at org.deeplearning4j.nn.graph.ComputationGraph.output(ComputationGraph.java:1686)
at org.deeplearning4j.nn.graph.ComputationGraph.output(ComputationGraph.java:1672)
at org.lungen.deeplearning.net.predictor.CharacterSequenceValuePredictorNet.testOutputAndScore(CharacterSequenceValuePredictorNet.java:195)
at org.lungen.deeplearning.net.predictor.CharacterSequenceValuePredictorNet.train(CharacterSequenceValuePredictorNet.java:166)
at org.lungen.deeplearning.net.predictor.CharacterSequenceValuePredictorNet.main(CharacterSequenceValuePredictorNet.java:283)
========== Memory Information ==========
----- Version Information -----
Deeplearning4j Version 1.0.0-beta3
Deeplearning4j CUDA deeplearning4j-cuda-10.0
----- System Information -----
Operating System Microsoft Windows 7 SP1
CPU Intel(R) Core(TM) i7-3770 CPU @ 3.40GHz
CPU Cores - Physical 4
CPU Cores - Logical 8
Total System Memory 15.97 GB (17144102912)
Number of GPUs Detected 1
Name CC Total Memory Used Memory Free Memory
GeForce GTX 750 5.0 2 GB (2147483648) 1.67 GB (1795002368) 336.15 MB (352481280)
----- ND4J Environment Information -----
Data Type FLOAT
backend CUDA
blas.vendor CUBLAS
os Windows 7
----- Memory Configuration -----
JVM Memory: XMX 7.67 GB (8232370176)
JVM Memory: current 7.67 GB (8232370176)
JavaCPP Memory: Max Bytes 7.67 GB (8232370176)
JavaCPP Memory: Max Physical 15.33 GB (16464740352)
JavaCPP Memory: Current Bytes 470.26 MB (493106209)
JavaCPP Memory: Current Physical 3.35 GB (3601498112)
Periodic GC Enabled true
Periodic GC Frequency 100 ms
----- Workspace Information -----
Workspaces: # for current thread 4
Current thread workspaces:
Name State Size # Cycles
WS_LAYER_WORKING_MEM CLOSED 117.40 MB (123100000) 6802
WS_ALL_LAYERS_ACT CLOSED 19.41 MB (20349840) 2400
WS_LAYER_ACT_0 CLOSED 6.23 MB (6528000) 1601
WS_LAYER_ACT_1 CLOSED 381.47 MB (400000000) 1601
Workspaces total size 524.50 MB (549977840)
Helper Workspaces
CUDNN_WORKSPACE 7.06 MB (7408000)
----- Network Information -----
Network # Parameters 1432106
Parameter Memory 5.46 MB (5728424)
Parameter Gradients Memory 5.46 MB (5728424)
Updater Number of Elements 2862812
Updater Memory 10.92 MB (11451248)
Updater Classes:
org.nd4j.linalg.learning.AdamUpdater
org.nd4j.linalg.learning.NoOpUpdater
Params + Gradient + Updater Memory 16.38 MB (17179672)
Iteration Count 400
Epoch Count 0
Backprop Type TruncatedBPTT
TBPTT Length 50/50
Workspace Mode: Training ENABLED
Workspace Mode: Inference ENABLED
Number of Layers 7
Layer Counts
BatchNormalization 2
DenseLayer 1
LSTM 3
OutputLayer 1
Layer Parameter Breakdown
Idx Name Layer Type Layer # Parameters Layer Parameter Memory
1 lstm-1 LSTM 403000 1.54 MB (1612000)
2 lstm-2 LSTM 501000 1.91 MB (2004000)
3 lstm-3 LSTM 501000 1.91 MB (2004000)
5 norm-1 BatchNormalization 1000 3.91 KB (4000)
6 dense-1 DenseLayer 25100 98.05 KB (100400)
7 norm-2 BatchNormalization 400 1.56 KB (1600)
8 output OutputLayer 606 2.37 KB (2424)
----- Layer Helpers - Memory Use -----
# Layer Name Layer Class Helper Class Total Memory Memory Breakdown
5 norm-1 BatchNormalization CudnnBatchNormalizationHelper 1.95 KB (2000) {meanCache=1000, varCache=1000}
7 norm-2 BatchNormalization CudnnBatchNormalizationHelper 800 B {meanCache=400, varCache=400}
Total Helper Count 2
Helper Count w/ Memory 2
Total Helper Persistent Memory Use 2.73 KB (2800)
----- Network Activations: Inferred Activation Shapes -----
Current Minibatch Size 100
Current Input Shape (Input 0) [100, 152, 2000]
Idx Name Layer Type Activations Type Activations Shape # Elements Memory
0 recurrentInput InputVertex InputTypeRecurrent(152,timeSeriesLength=2000) [100, 152, 2000] 30400000 115.97 MB (121600000)
1 lstm-1 LSTM InputTypeRecurrent(250,timeSeriesLength=2000) [100, 250, 2000] 50000000 190.73 MB (200000000)
2 lstm-2 LSTM InputTypeRecurrent(250,timeSeriesLength=2000) [100, 250, 2000] 50000000 190.73 MB (200000000)
3 lstm-3 LSTM InputTypeRecurrent(250,timeSeriesLength=2000) [100, 250, 2000] 50000000 190.73 MB (200000000)
4 thoughtVector LastTimeStepVertex InputTypeFeedForward(250) [100, 250] 25000 97.66 KB (100000)
5 norm-1 BatchNormalization InputTypeFeedForward(250) [100, 250] 25000 97.66 KB (100000)
6 dense-1 DenseLayer InputTypeFeedForward(100) [100, 100] 10000 39.06 KB (40000)
7 norm-2 BatchNormalization InputTypeFeedForward(100) [100, 100] 10000 39.06 KB (40000)
8 output OutputLayer InputTypeFeedForward(6) [100, 6] 600 2.34 KB (2400)
Total Activations Memory 688.44 MB (721882400)
Total Activation Gradient Memory 688.44 MB (721880000)
----- Network Training Listeners -----
Number of Listeners 3
Listener 0 org.x.deeplearning.listener.ScorePrintListener@7b78ed6a
Listener 1 ScoreIterationListener(10)
Listener 2 org.x.deeplearning.listener.UIStatsListener@6fca5907