Adversarial Spoofing Attacks

From Antispoofing Wiki

Problem Overview & Certification

Adversarial Spoofing Attacks (AXs) refer to a malicious technique, in which spoofing data is presented as genuine to the authentication system. This type of attack exploits the lack of robustness in a Deep Neural Network (DNN), which is responsible for the correct response and decision-making of a biometric solution. The AXs attack tools are called adversarial examples.

AXs shouldn't be confused with Presentation Attacks (PAs) as they don't involve forged biometric traits. Instead, fraudsters employ input data with added noises — or perturbations — which leads to instability in a DNN's work. Visual recognition models are the top targets in this scenario, although voice or text recognition solutions are also at risk.

Since AXs jeopardize safety-critical systems — like autonomous cars or runway alignment used in aviation — it is of vital importance to assess their robustness. DNN certification is challenging due to their black box architecture, complexity, large size, and so on.

Such a certification aims to examine the lower bound of a DNN's robustness when it faces an adversarial spoofing attack. As of now a few methods are offered to attest DNNs: DeepTest, SoK, Reluplex, and others.

Threat Model of Adversarial Spoofing Attack

AXs have a common goal of causing a misclassification with a certain perturbation added to an input data. The phenomenon itself was first observed in 2014 in a paper by Szegedy et al. However, methods to achieve that may vary. Authors mention a number of adversarial techniques.

One of them employs the Fast Gradient Sign Method (FGSM) described by Goodfellow et al. This attack technique includes making a prediction on the image with a Convolutional Neural Network (CNN), calculating its loss with the true class label, estimating the gradient loss and sign, and other steps.

The Carlini-Wagner model proposes a minimal perturbation usage, which is possible due to optimization problem-solving. Authors review 7 objective functions, while also pointing out that a loss function proposed by Szegedy et al. isn't effective due to its complexity and high non-linearity. Instead, they offer the most efficient objective function:

[math]\displaystyle{ f(x^t)=\max(\max\{Z(x^t)i : i \neq t\} - Z(x^t)t,-k) }[/math]


  • [math]\displaystyle{ Z(x^t) }[/math] — probability vector.
  • [math]\displaystyle{ \max\{Z(x^t)i : i \neq t\} }[/math] — non-target class highest probability.
  • [math]\displaystyle{ \max\{Z(x^t)i : i \neq t\} - Z(x^t) }[/math] — difference between the actual visual data and a probable misidentified object.

Based on this objective function, it's possible to fashion a high-confidence adversarial spoofing attack.

Another model proposed by Rozsa et al. offers an alternative attack type. It focuses on internal layer representation alignment with the target image. This technique allows producing AXs and manipulate the feature representation, which is extracted by the DNNs for facial verification, among all else.

Types of Adversarial Attacks

AXs are separated into three categories.

Evasion attacks

Evasion attacks are a widespread type. A malicious actor attempts to avoid a biometric system by adjusting the spoofed samples at the testing stage.

Poisoning attacks

This type aims at infecting (poisoning) the genuine training data with the adversarial examples. It is produced during the training stage with the goal of either decreasing its accuracy or completely sabotaging a deep learning solution.

Exploratory attacks

This final type is more insidious in its nature. It seeks to expose the training algorithms of a biometric solution, as well as explore its training datasets. Then, based on the obtained knowledge, adversarial samples of high quality can be crafted.

Review of the Most Well-known Adversarial Spoofing Attacks

Along with the above-mentioned FGSM and Carlini-Wagner attacks, experts mention some other noteworthy AXs types. One of them, Jacobian-based Saliency Map Attack (JSMA) is a minimalistic technique, which allows altering only a few pixels in the visual data to spoof a system.

A similar approach is dubbed One pixel attack. It includes a lengthy process of creating a number of [math]\displaystyle{ R^5 }[/math] vectors with [math]\displaystyle{ xy }[/math]-coordinates and RGB values and random modification of their elements to breed 'parents-children', which will eventually allow creating the most fit pixel candidate.

The DeepFool method allows architecting a minimal norm adversarial perturbation. This is possible due to region boundary linearization, perturbation accumulation, and other techniques. It is reported to be more effective than FGSM.

Basic & Least-Likely-Class Iterative Method (BIM + ILCM) is based on a simple concept of one-step increase of the loss of the classifier, which is repeated in multiple iterative smaller steps. At the same time, whenever a step is made, the direction of the model gets adjusted.

Expectation Over Transformation (EOT) is a peculiar method, which employs texture, camera distance, lighting, pose, and solid-color background manipulations — this know-how is known as "distribution of image/object transformations".

Main Directions in Defense Against Adversarial Spoofing Attacks

Three main approaches are suggested to prevent AXs.

Modified Training/Input

This group of techniques includes:

  • Brute-force adversarial training with strong attacks.
  • Data compression as defense with JPG compression on the FGSM-based perturbations.
  • Foveation-based defense that applies a DNN to various image regions.
  • Data randomization, which implies that attacks decrease in efficacy if random resizing and padding — addition of a number of pixels to an image — are applied to the adversarial examples.

Gaussian data augmentation is also seen as a promising preventive measure.

Modifying the Network

Featured techniques are:

  • Deep Contractive Networks with the smoothness penalty applied to them during training.
  • Gradient regularization featuring penalization of the degree of variation occurring in DNNs.
  • Defensive distillation implying knowledge transfer between bigger and smaller networks.
  • Biologically inspired protection enhances a system with highly non-linear activations.
  • Parseval Networks introduce layer-wise regularization through the network's global Lipschitz constant regulation.
  • DeepCloak offers inserting a masking layer before a classification layer.

Other know-hows include bounded ReLU, statistical filtering, output layer modification, additive noise usage, etc.

Network Add-ons

This ensemble of techniques suggests:

  • Defense against universal perturbations with appending extra pre-input layers.
  • GAN-based defense with the training process overseen by a Generative Adversarial Network.
  • Detection Only methods that include feature squeezing, external detectors, etc.

Other solutions include scalar quantization and spatial smoothing, persistent homology application, and so on.

Countermeasures Against Adversarial Spoofing Attacks

Other defense mechanisms include:

  • Gradient hiding. Suggests that the gradient information should be hidden from the potential attackers.
  • Blocked transferability. DNN's transferability causes different classifiers to 'repeat' each other's mistakes, hence it must be blocked.
  • MagNet. It uses a classifier as a black box, but avoids modifying it. Instead, detectors differentiate bona fide and adversarial samples.

High‐Level Representation Guided Denoiser (HGD) is also a promising tool, as it's capable of noise removal from the images in question with a loss function.


There are at least two contests dedicated to the problem: Adversarial Attacks and Defenses Competition with two datasets DEV and FINAL and NIPS 2017 Adversarial Learning Competition.

Adversarial Spoofing in Voice Biometrics

Automatic Speaker Verification systems (ASV) are also vulnerable to AXs. To prevent them, a Generative Adversarial Network for Biometric Anti-Spoofing (GANBA) was developed. Its architecture allows generating attacks, while also strengthening the discriminator responsible for Presentation Attack Detection (PAD).

GANBA, separated into White and Black box models, employs short-time Fourier transform features, Mel-frequency cepstral coefficients, Time Delay Neural Network (TDNN), and other components.

Adversarial Attacks in Natural Language Processing

As for Natural Language Processing (NLP), its DNN models, like Seq2Seq or Recurrent Neural Network (RNN), can also be spoofed with perturbations created with the likes of forward derivatives. They target various components: from Optical Character Recognition (OCR) to Visual-Semantic Embeddings (VSE). The proposed defense measures include adversarial training, model regularization, etc.

Other Instances of Adversarial Spoofing

AXs are also reported to be targeting object recognition, video authentication, automatic spam and malware filtering, reinforced learning, and so on.


  1. Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
  2. How Neural Networks are Already Showing Future Potential for Aerospace
  3. Intriguing properties of neural networks
  4. Universal Adversarial Spoofing Attacks against Face Recognition
  5. Explaining and Harnessing Adversarial Examples
  6. Adversarial attacks with FGSM (Fast Gradient Sign Method)
  7. Towards Evaluating the Robustness of Neural Networks
  8. LOTS about Attacking Deep Features
  9. Remote sensing by Wikipedia
  10. Region-Wise Deep Feature Representation for Remote Sensing Images
  11. A survey on adversarial attacks and defences
  12. Illustration of exploratory attacks on a machine learning based spam filtering system
  13. Probabilistic Jacobian-based Saliency Maps Attacks
  14. Artificial Intelligence-Powered Systems and Applications in Wireless Networks
  15. Synthesizing Robust Adversarial Examples
  16. Padding (Machine Learning)
  17. Smoothness constraints in Deep Learning
  18. Defensive distillation scheme
  19. Detecting Adversarial Examples in Deep Neural Networks
  20. Persistent homology
  21. NDSS- Feature Squeezing Mitigates and Detects Carlini-Wagner Adversarial Examples
  22. Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser
  23. Adversarial Attacks and Defences Competition
  24. NIPS17 Adversarial learning - Final results
  25. DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars
  26. GANBA: Generative Adversarial Network for Biometric Anti-Spoofing
  27. Attacking Natural Language Processing Systems With Adversarial Examples
  28. Adversarial Attacks and Defenses in Images, Graphs and Text: A Review