MFABA: A More Faithful and Accelerated Boundary-based Attribution Method for Deep Neural Networks

Figure 1: Linear and non-linear path of the direction for adversarial attack and baseline samples.

Abstract

To better understand the output of deep neural networks (DNN), attribution based methods have been an important approach for model interpretability, which assign a score for each input dimension to indicate its importance towards the model outcome. Notably, the attribution methods use the axioms of sensitivity and implementation invariance to ensure the validity and reliability of attribution results. Yet, the existing attribution methods present challenges for effective interpretation and efficient computation. In this work, we introduce MFABA, an attribution algorithm that adheres to axioms, as a novel method for interpreting DNN. Additionally, we provide the theoretical proof and in-depth analysis for MFABA algorithm, and conduct a large scale experiment. The results demonstrate its superiority by achieving over 101.5142 times faster speed than the state-of-the-art attribution algorithms. The effectiveness of MFABA is thoroughly evaluated through the statistical analysis in comparison to other methods.

Environment Setup

To replicate our experiments, ensure your environment meets the following prerequisites:

• Python 3.8+
• foolbox==3.3.3
• numpy==1.24.2
• pandas==1.5.3
• Pillow==9.5.0
• PyTorch==2.0.0+cu118
• torchvision==0.15.1+cu118
• tqdm==4.65.0

Compute Attribution

Complete examples are shown in example.ipynb.Here are some sample code.

from saliency.saliency_zoo import mfaba_sharp

# Load your model
model = load_model(...)
model.to(device)
model.eval()

# Load your data
img_batch = torch.load("data/img_batch.pt") # img_batch.shape = (1000,3,224,224)
target_batch = torch.load("data/label_batch.pt") # target_batch.shape = (1000,)

# Set batch_size
batch_size = 128
attributions = [] # init attributions

# Caculate attribution
for i in range(0, len(img_batch), batch_size):
    img = img_batch[i:i+batch_size].to(device)
    target = target_batch[i:i+batch_size].to(device)
    attributions.append(mfaba_sharp(model, img, target))
if attributions[0].shape.__len__() == 3:
    attributions = [np.expand_dims(attribution, axis=0) for attribution in attributions]
attributions = np.concatenate(attributions, axis=0)

Results

Table 1: Insertion score (the higher the better), deletion score (the lower the better), and AUC (the higher the better), SMOOTH and SHARP are variants of our Method MFABA.

Model	Method	Insertion Score	Deletion Score	AUC
Inception-v3	SM	0.2792	0.0445	0.5150
Inception-v3	IG	0.3215	0.0445	0.5180
Inception-v3	BIG	0.4840	0.0557	0.5200
Inception-v3	AGI	0.4629	0.0590	0.5178
Inception-v3	SMOOTH	0.5368	0.0640	0.5389
Inception-v3	SHARP	0.5407	0.0627	0.5367
ResNet-50	SM	0.1441	0.0387	0.4714
ResNet-50	IG	0.1467	0.0302	0.4823
ResNet-50	BIG	0.2911	0.0485	0.4759
ResNet-50	AGI	0.3695	0.0383	0.4772
ResNet-50	SMOOTH	0.3211	0.0574	0.4854
ResNet-50	SHARP	0.3237	0.0566	0.4857
VGG16	SM	0.1018	0.0297	0.4257
VGG16	IG	0.0973	0.0249	0.4431
VGG16	BIG	0.2274	0.0390	0.4356
VGG16	AGI	0.2910	0.0320	0.4359
VGG16	SMOOTH	0.2808	0.0424	0.4540
VGG16	SHARP	0.2856	0.0410	0.4540

Table 2: FPS Results of BIG, IG, AGI, MFABA Algorithms.

Dataset	Method	BIG	IG	AGI	MFABA
CIFAR10	ResNet-50	1.35	24.64	0.14	136.96
CIFAR100	ResNet-50	1.36	24.29	0.15	162.74
ImageNet	ResNet-50	0.37	6.76	0.28	51.58
ImageNet	EfficientNet	0.32	10.42	0.21	39.97

Citation

For academic usage, please cite our paper:

@article{zhu2023mfaba,
  title={MFABA: A More Faithful and Accelerated Boundary-based Attribution Method for Deep Neural Networks},
  author={Zhu, Zhiyu and Chen, Huaming and Zhang, Jiayu and Wang, Xinyi and Jin, Zhibo and Xue, Minhui and Zhu, Dongxiao and Choo, Kim-Kwang Raymond},
  journal={arXiv preprint arXiv:2312.13630},
  year={2023}
}