h�b```b``vb`2f12 � P�������I��G��[��T���;,�L�^pgP���e���|�q���>8�pkZ�͞�l���9׶�a,��U�S���er��U�a�n3&i��ݑ�/��L�+�q�d���l,PY������8Ʉ�P�$�euf�|��ى ����|����Ͷ�h��6�����ם�X We define the original correct example as Imageorig and the corresponding adversarial example as Imageadv={Image1,Image2,...,Image10}. To answer RQ2, we use the training data set of each model as the input set to calculate the original neural coverage, and the generated adversarial example set as the input set to calculate the neural coverage of CAGFuzz. We further expand the training data by adding the same number of new generated examples, and train DNNs by 5 epochs. If the adversarial examples increase the coverage of the target DNN, they will be stored in the processing pool and set a time priority (Lines 16- 19). There are two mappings G and F in the model. We retrain the DNN model by mixing 65% of the adversarial example set and the original training set, and then validate the DNN model with the remaining 35% of the adversarial example set and the original test set on the original model and the retraining model. 6 To further validate this approach, we train LeNet-1, LeNet-4 and LeNet-5 with 60,000 original images. defense system was not “capable of putting the prosecution’s case to meaningful adversarial testing” so that “courts cannot ensure that their decisions, judgments, verdicts and punishments are rendered fairly and accurately.”16 Fresno County agreed to increase funding of public defense A typical approach generates adversarial examples from the perspective of model. Song, T. Kim, S. Nowozin, S. Ermon, and N. Kushman, “Pixeldefend: At present, we only use neuron coverage to guide the generation of adversarial examples. Moreover, it can also be found from the figure that the retraining model is more stable and has a smaller change range during the training process. The features in VGG-19 model are extracted according to the hierarchy. 0000001373 00000 n Considering the difference of datasets of different target DL systems, such as some DL systems with label data and other DL systems may not, we choose CycleGAN [19] as the training model of adversarial example generator, since CycleGAN does not require the matching of data sets and label information. the target DNN. When counsel is totally absent, is prevented from assisting the accused at a critical stage of the proceeding, or when counsel entirely fails to subject the prosecution's case to meaningful adversarial testing, courts will presume prejudice. The LeNet model works well, but when we train VGG-16 network, we find that the accuracy of the model is basically stable after 50 training epochs, as shown in Fig.

The priority of the example determines which kind of examples should be selected next time. For example, a DNN for image classification consists of 100 neurons.

In the following, we describe the main threats to validity of our approach in detail. features of the original and adversarial examples, and constrain the DeepXplore performs gradient ascent to solve a joint optimization problem that maximizes both neuron coverage and the number of potentially erroneous behaviors. Superion [39] conceptually extends LangFuzz [40] with coverage-guided: the seeds of structural mutation that increase coverage are retained to further fuzzing.
In general, Fig. The input formats of DNN can be various. ∙ These coverage criteria can be used as guidance for generating test examples. 11. To make matters worse, mutations in these adversarial examples may bury other meaningful examples in a fuzzy queue, significantly reducing the fuzzing effect. According to the domain, it is used as the input of the cycle generative adversarial network to train the adversarial example generator. In general, as can be seen from the comparisons, the generalization ability of the adversarial examples generated based on data is better than that based on the model. For example, in Fig. To answer RQ4, we add adversarial examples generated by CAGFuzz to the training set to retrain the DNN model and measure whether it can improve the accuracy of the target DNN. Finally, we retrain We define the data distribution of two groups of data domains, where data domain X is expressed as x∼Pdata(x), and data domain Y is expressed as y∼Pdata(y). We choose MNIST data set as the sampling set, and sampling 10 examples for each class in the training set and 4 examples for each class in the test set. two every Convl1-Convl2, four every Convl3-Convl5, and three full-connection layers, Fc6, Fc7 and Fc8. Some approaches considering small perturbations from the perspective of DNN model.
For example, example x of data domain X is generated by mapping function P to generate adversarial example y′, and then adversarial example y′ is generated by mapping function Q to generate new adversarial example x′. When fault is found, it is also very difficult to locate the exact position in the original DL systems. The behavior consistency between different implementations acts as Oracle to detect functional defects. 0000003889 00000 n DeepXplore [10].


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