Basic Usage#

DNNF can be run on correctness problems specified using ONNX and DNNP. DNNP is the same property specification language used by the DNNV verifier framework. A description of this specification language can be found in the DNNV documentation.

To execute DNNF, first activate the virtual environment with:

$ . .venv/bin/activate

This is only required if DNNF was installed from source. The virtual environment should open automatically if using the docker image or the provided VM.

The DNNF tool can then be run as follows:

$ dnnf PROPERTY --network NAME PATH

Where PROPERTY is the path to the property specification, NAME is the name of the network used in the property specification (typically N), and PATH is the path to a DNN model in the ONNX format.

To see additional options, run:

$ dnnf -h
usage: dnnf [-h] [--long-help] [-V] [--seed SEED] [-v | -q] [-N NAME PATH]
            [--save-violation PATH] [--vnnlib] [-p N_PROC] [--n_starts N_STARTS]
            [--cuda] [--backend BACKEND [BACKEND ...]] [--set BACKEND PARAM VALUE]

dnnf - deep neural network falsification

positional arguments:

optional arguments:
  -h, --help            show this help message and exit
  --long-help           show a longer help message with available falsifier backends and exit
  -V, --version         show program's version number and exit
  --seed SEED           the random seed to use (default: None)
  -v, --verbose         show messages with finer-grained information (default: False)
  -q, --quiet           suppress non-essential messages (default: False)
  -N, --network NAME PATH
  --save-violation PATH
                        the path to save a found violation (default: None)
  --vnnlib              use the vnnlib property format (default: None)
  -p, --processors, --n_proc N_PROC
                        The maximum number of processors to use (default: 1)
  --n_starts N_STARTS   The default number of random starts per sub-property (can be set per backend with --set) (default: -1)
  --cuda                use cuda (default: False)
  --backend BACKEND [BACKEND ...]
                        the falsification backends to use (default: ['pgd'])
                        set parameters for the falsification backend (default: None)

To see the currently available falsification backends, use the --long-help option.

Running on Benchmarks#

We provide several DNN verification benchmarks in DNNP and ONNX formats in dlshriver/dnnv-benchmarks. This benchmark repository includes both DNNF-GHPR and the DNNF-CIFAR-EQ benchmarks introduced by DNNF!

To execute DNNF on a problem in one of the benchmarks, first navigate to the desired benchmark directory in benchmarks (e.g., DNNF-GHPR, DNNF-GHPR). Then run DNNF as specified above. For example, to run DNNF with the Projected Gradient Descent adversarial attack from cleverhans on an DNNF-GHPR property and network, run:

$ cd benchmarks/DNNF-GHPR
$ dnnf properties/ --network N onnx/dronet.onnx --backend cleverhans.projected_gradient_descent

Which will produce output similar to:

Falsifying: Forall(x, (((0 <= x) & (x <= 1) & (N[(slice(2, -3, None), 1)](x) <= -2.1972245773362196)) ==> ((-0.08726646259971647 <= N[(slice(2, -1, None), 0)](x)) & (N[(slice(2, -1, None), 0)](x) <= 0.08726646259971647))))

  result: sat
  falsification time: 0.6901
  total time: 2.3260

The available backends for falsification are:

  • CleverHans

    • cleverhans.carlini_wagner_l2

    • cleverhans.fast_gradient_method

    • cleverhans.hop_skip_jump_attack

    • cleverhans.projected_gradient_descent

    • cleverhans.spsa

  • FoolBox

    • foolbox.ATTACK where ATTACK is the name of an adversarial attack from this list

  • TensorFuzz

    • tensorfuzz

Attack specific parameters can be set using the --set BACKEND NAME VALUE option. For example, to set the nb_iter parameter of the cleverhans.projected_gradient_descent attack to 40 steps, you can specify --set cleverhans.projected_gradient_descent nb_iter 40.

If a property uses parameters, then the parameter value can be set using --prop.PARAMETER=VALUE, e.g., --prop.epsilon=1, similar to DNNV.