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Sponge Weight Poisoning: Increasing Energy Consumption of Deep Neural Networks by Directly Altering Model Parameters


Core Concepts
The SpongeNet attack directly alters the parameters of pre-trained deep neural network models to increase their energy consumption during inference, without significantly affecting the model's accuracy.
Abstract
The paper proposes a novel sponge attack called SpongeNet, which is the first sponge attack that directly alters the parameters of a pre-trained model, rather than the input data or the training objective. Key highlights: SpongeNet can increase the energy consumption of vision models by up to 11% and generative models like StarGAN by up to 5.3%, with minimal impact on accuracy or generation quality. SpongeNet is more stealthy than previous sponge attacks, as it does not require significant changes to the model's weights. SpongeNet is effective even when the attacker has access to only 1% of the dataset, making it more practical than the previous Sponge Poisoning attack. Defenses like parameter perturbations and fine-pruning are ineffective against SpongeNet unless specifically adapted to target the biases of the affected layers. A user study confirms that SpongeNet produces images that are visually closer to the original than those generated by Sponge Poisoning.
Stats
The paper reports the following key metrics: Energy ratio increase of up to 11% for vision models and 5.3% for StarGAN Accuracy drop of up to 5% for vision models and SSIM drop of up to 0.11 for StarGAN
Quotes
"SpongeNet is the first sponge attack that alters the parameters of pre-trained models." "SpongeNet can successfully increase the energy consumption of vision models with fewer samples required than Sponge Poisoning." "SpongeNet is stealthier than the previous Sponge Poisoning attack as it does not require significant changes in the victim model's weights."

Deeper Inquiries

How could the SpongeNet attack be extended to target other types of neural network architectures beyond vision and generative models

To extend the SpongeNet attack to target other types of neural network architectures beyond vision and generative models, several modifications and considerations can be made: Natural Language Processing (NLP) Models: For NLP models like transformers, the attack could focus on altering the attention mechanisms or the embeddings. By increasing the biases or altering the parameters in these critical components, the attacker could disrupt the sparsity and increase energy consumption during inference. Recurrent Neural Networks (RNNs): In RNNs, the attack could target the recurrent connections or the hidden states. By manipulating the biases or weights in these components, the attacker could introduce more non-zero activations, impacting the energy consumption of the model. Graph Neural Networks (GNNs): For GNNs, the attack could target the message passing mechanisms or the aggregation functions. By modifying the parameters related to these operations, the attacker could disrupt the sparsity patterns and increase energy consumption during inference. Audio Processing Models: In models designed for audio processing, such as speech recognition or music generation, the attack could focus on the layers responsible for feature extraction or waveform processing. By altering the biases or weights in these layers, the attacker could induce energy consumption effects similar to those seen in vision models. Hybrid Models: For models that combine different modalities or tasks, the attack could be adapted to target specific components related to each modality. By identifying the critical layers or parameters in each modality, the attacker could tailor the attack to disrupt sparsity and increase energy consumption effectively. By understanding the unique characteristics and components of different neural network architectures, the SpongeNet attack can be customized and extended to target a wide range of models beyond vision and generative models.

What other defenses beyond parameter perturbations and fine-pruning could be effective against the SpongeNet attack

In addition to parameter perturbations and fine-pruning, several other defenses could be effective against the SpongeNet attack: Layer-wise Activation Constraints: By imposing constraints on the activation values of specific layers, defenders can limit the impact of bias alterations introduced by the attack. By monitoring and regulating the activation patterns, anomalies caused by SpongeNet can be detected and mitigated. Dynamic Parameter Adjustment: Implementing mechanisms that dynamically adjust the model parameters based on real-time performance metrics can help counteract the effects of SpongeNet. By continuously monitoring the energy consumption and accuracy levels, the model can adapt its parameters to maintain efficiency and performance. Adversarial Training: Training the model with adversarial examples that mimic the effects of SpongeNet can help the model learn to resist such attacks. By exposing the model to perturbed inputs during training, it can develop robustness against similar manipulation strategies. Ensemble Learning: Utilizing ensemble learning techniques where multiple models make predictions collaboratively can enhance the model's resilience against attacks like SpongeNet. By combining the outputs of diverse models, the system can mitigate the impact of individual models being compromised. Regularization Techniques: Applying regularization methods such as dropout or weight decay can help prevent overfitting and limit the influence of biased parameters introduced by the attack. By promoting simpler models and reducing parameter magnitudes, the model can maintain stability and resist adversarial manipulations. By incorporating a combination of these defenses alongside parameter perturbations and fine-pruning, deep learning systems can enhance their security posture against the SpongeNet attack and similar threats.

Could the SpongeNet attack be combined with other types of adversarial attacks, such as evasion or backdoor attacks, to create more comprehensive threats to deep learning systems

The SpongeNet attack could be combined with other types of adversarial attacks, such as evasion or backdoor attacks, to create more comprehensive threats to deep learning systems: Evasion Attacks: By combining SpongeNet with evasion attacks, the attacker could manipulate the model's parameters to not only increase energy consumption but also to introduce vulnerabilities that misclassify specific inputs. This dual-threat approach could lead to both availability and integrity issues in the system. Backdoor Attacks: Integrating backdoor mechanisms into the model alongside the SpongeNet attack could create a scenario where the model exhibits increased energy consumption under specific conditions or inputs. This hidden behavior, combined with the energy increase, could make the attack more stealthy and challenging to detect. Data Poisoning: Leveraging data poisoning techniques in conjunction with SpongeNet could further compromise the model's performance and energy efficiency. By injecting malicious samples into the training data, the attacker can manipulate the model's behavior and energy consumption patterns, amplifying the impact of the attack. Model Inversion: Incorporating model inversion techniques with SpongeNet could enable the attacker to extract sensitive information from the model while simultaneously disrupting its energy consumption patterns. This combined attack could compromise both the confidentiality and availability of the system. By combining SpongeNet with other adversarial strategies, attackers can create multifaceted threats that target different aspects of deep learning systems, posing significant challenges for defenders in ensuring the security and reliability of these systems.
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