SPF needs JPF-Core to work, so it is essential that JPF-Core is installed first.
JPF-Core is also provided as part of this repository. JPF-Core can be downloaded from https://github.com/javapathfinder/jpf-core. Detailed instructions are provided at the aforementioned repository.
The following subsection gives the instructions on installing SPF. These instructions are taken from the original SPF GitHub Repository. https://github.com/SymbolicPathFinder/jpf-symbc
This JPF extension provides symbolic execution for Java bytecode. It performs a non-standard interpretation of byte-codes. It allows symbolic execution on methods with arguments of basic types (int, long, double, boolean, etc.). It also supports symbolic strings, arrays, and user-defined data structures.
SPF now has a "symcrete" mode that executes paths triggered by concrete inputs and collects constraints along the paths
A paper describing Symbolic PathFinder appeared at ISSTA'08:
Title: Combining Unit-level Symbolic Execution and System-level Concrete Execution for Testing NASA Software, Authors: C. S. Pasareanu, P. C. Mehlitz, D. H. Bushnell, K. Gundy-Burlet, M. Lowry, S. Person, M. Pape. (DOI: https://dl.acm.org/doi/10.1145/1390630.1390635)
First of all please use Java 8 (I am afraid our tools do not work with older versions of Java).
Then please download jpf-core from here: https://github.com/yannicnoller/jpf-core/tree/0f2f2901cd0ae9833145c38fee57be03da90a64f
And jpf-symbc from here: https://github.com/SymbolicPathFinder/jpf-symbc
Import them in Eclipse as 2 Java projects. Also create a .jpf dir in your home directory and create in it a file called "site.properties" with the following content:
jpf-core = <path-to-jpf-core-folder>/jpf-core
jpf-symbc = <path-to-jpf-core-folder>/jpf-symbc
extensions=${jpf-core},${jpf-symbc}
You can then try to run some examples by selecting a .jpf file from the "examples" directory and then selecting a run configuration from the "Run" menu in Eclipse. In particular you should select: "run-JPF-symbc" to run Symbolic PathFinder on your example (configuration "run-JPF-symbc-mac" is tailored for Mac).
Good luck!
Additional Steps:
- json-simple.jar, located in the lib folder https://github.com/muhammadusman93/neurospf/blob/main/jpf-symbc/lib/json_simple.jar, should be added to the build path.
Command to run the Keras to Java Translator
python translator/java-dnn-gen.py --model kerasmodels/<kerasmodel>.h5 --outputs "<path to jpf-symbc>/jpf-symbc/src/examples/neurospf" -d <image file>
Sample Command to run the demo example.
python translator/java-dnn-gen.py --model kerasmodels/mnist-lowquality.h5 --outputs "<path to jpf-symbc>/jpf-symbc/src/examples/neurospf" -d demoimage
The above command will generate the required code files inside jpf-symbc/src/examples/neurospf directory. User can run the SPF-DNN.jpf file using jpf-symbc to generate adversarial images
DNN architecture is as follows.
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 26, 26, 2) 20
_________________________________________________________________
activation_1 (Activation) (None, 26, 26, 2) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 24, 24, 4) 76
_________________________________________________________________
activation_2 (Activation) (None, 24, 24, 4) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 12, 12, 4) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 576) 0
_________________________________________________________________
dense_1 (Dense) (None, 128) 73856
_________________________________________________________________
activation_3 (Activation) (None, 128) 0
_________________________________________________________________
dense_2 (Dense) (None, 10) 1290
_________________________________________________________________
activation_4 (Activation) (None, 10) 0
=================================================================
Total params: 75,242
Trainable params: 75,242
Non-trainable params: 0
DNN architecture is as follows.
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 26, 26, 8) 80
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 13, 13, 8) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 11, 11, 16) 1168
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 5, 5, 16) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 400) 0
_________________________________________________________________
dense_1 (Dense) (None, 100) 40100
_________________________________________________________________
dense_2 (Dense) (None, 10) 1010
=================================================================
Total params: 42,358
Trainable params: 42,358
Non-trainable params: 0
DNN architecture is as follows.
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 30, 30, 32) 896
_________________________________________________________________
activation_1 (Activation) (None, 30, 30, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 28, 28, 32) 9248
_________________________________________________________________
activation_2 (Activation) (None, 28, 28, 32) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 14, 14, 32) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 12, 12, 64) 18496
_________________________________________________________________
activation_3 (Activation) (None, 12, 12, 64) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 10, 10, 64) 36928
_________________________________________________________________
activation_4 (Activation) (None, 10, 10, 64) 0
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 1600) 0
_________________________________________________________________
dense_1 (Dense) (None, 512) 819712
_________________________________________________________________
activation_5 (Activation) (None, 512) 0
_________________________________________________________________
dense_2 (Dense) (None, 10) 5130
_________________________________________________________________
activation_6 (Activation) (None, 10) 0
=================================================================
Total params: 890,410
Trainable params: 890,410
Non-trainable params: 0
- Muhammad Usman ([email protected])
This project is licensed under the MIT License - see the LICENSE file for details