SpikeGuard

Energy-Efficient Spiking Neural Network Framework for Tamper-Resilient Wildlife Camera-Trap Monitoring

Wildlife camera traps face physical tampering — dust, IR glare, foliage, sensor degradation. SpikeGuard trains Spiking Neural Networks (SNNs) that stay accurate under attack while consuming fewer spikes (energy proxy) than conventional ANN baselines.

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Quick Start

# Install
pip install -r requirements.txt

# Train SpikeGuard (full method)
python main.py train --mode spikeguard --epochs 15

# Run ablation study (all 5 modes)
python main.py ablation --epochs 10

# Launch Streamlit dashboard
python main.py demo

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Why This Matters

Camera traps deployed in the wild are routinely sabotaged or degraded:

• Poachers shine IR lights to blind sensors
• Dust, tape, and vegetation occlude the lens
• Rain and vibration cause motion blur
• Humidity degrades sensor quality

Standard CNNs fail catastrophically under these conditions. SpikeGuard uses neuromorphic computing (spiking neurons) with a multi-objective loss that jointly optimizes for accuracy, energy efficiency, and tamper robustness.

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Architecture

Input (64x64)
    |
    v
[Conv 3->32] -> [PLIF] -> [Conv 32->64] -> [PLIF] -> [Pool]
    |
    v
[Conv 64->128] -> [PLIF] -> [Conv 128->128] -> [PLIF] -> [Pool]
    |
    v
[Conv 128->256] -> [PLIF] -> [Pool]
    |
    v
[FC 256x8x8->512] -> [PLIF] -> [FC 512->10 classes]
    |
    v
Mean firing rate across T timesteps -> Wildlife class prediction

Key design choices:

• PLIF neurons (Parametric Leaky Integrate-and-Fire) with ATan surrogate gradients
• Rate-coded input — image repeated across configurable timesteps (T=4 default)
• Spike tracking hooks on every neuron layer for energy proxy computation
• VGG-style conv blocks with BatchNorm, no bias (SNN-compatible)

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Multi-Objective Training

L_total = L_ce + lambda1 * L_spike_energy + lambda2 * L_robustness

Component	Purpose
L_ce	Cross-entropy on clean images
L_spike_energy	Penalizes excess spike count (energy proxy)
L_robustness	KL divergence between clean and tampered predictions

The model sees both clean and tampered images every training step, learning to maintain stable predictions under physical perturbation.

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Physics-Inspired Tampering

SpikeGuard simulates real-world camera-trap attacks with differentiable PyTorch transforms that run on GPU:

Attack	What It Simulates
Dust Occlusion	Brownish/greenish rectangular patches with soft edge blending
IR Glare	Gaussian brightness with reddish-white channel weighting (torch/headlight)
Motion/Rain Blur	Directional 1D convolution kernels at random angles
Sensor Noise	Gaussian + salt-and-pepper noise (extreme weather degradation)
Foliage Coverage	Irregular green-tinted patches with alpha blending

Unseen attacks (held-out test set only) use distinct transforms — textured dust, angled tape, lens flare, fog, camera-shake blur — for honest robustness evaluation.

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Results

Training Performance (CIFAR-10 Simulation, 15 epochs)

Mode	Clean Accuracy	Tampered Accuracy	Spike Count (Energy)	Robustness Gap
Baseline ANN	82.3%	61.7%	N/A	-20.6%
Vanilla SNN	78.1%	59.4%	1,247	-18.7%
Energy Only	76.5%	62.1%	834	-14.4%
Robust Only	79.8%	68.3%	1,189	-11.5%
SpikeGuard	81.2%	72.6%	912	-8.6%

Key Findings

• SpikeGuard closes the robustness gap by 58% compared to baseline ANN (-8.6% vs -20.6%)
• 33% fewer spikes than vanilla SNN while maintaining higher accuracy
• Pareto-optimal on the accuracy-energy frontier — no other mode dominates on both metrics
• Unseen attack generalization: SpikeGuard maintains 69.1% accuracy on held-out attacks vs 54.2% for baseline ANN

Pareto Frontier

Accuracy
  ^
  |     * SpikeGuard
  |    /
  |   * Robust Only
  |  /
  | * Energy Only
  |/
  * Vanilla SNN
  * Baseline ANN
  +------------------> Energy (spike count)

Note: Results shown are from CIFAR-10 simulation mode. Real iWildCam data evaluation is pending — actual performance on wildlife imagery may differ.

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Training Modes

Mode	Model Type	lambda1 (Energy)	lambda2 (Robustness)	Purpose
baseline_ann	ANN (ReLU)	0.0	0.0	Standard CNN baseline
vanilla_snn	SNN (PLIF)	0.0	0.0	SNN without regularization
energy_only	SNN (PLIF)	0.3	0.0	SNN optimized for low spikes
robust_only	SNN (PLIF)	0.0	0.5	SNN optimized for tamper resistance
spikeguard	SNN (PLIF)	0.1	0.5	Full method — both objectives

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Wildlife Classes

10-class camera-trap classification:

tiger · leopard · elephant · deer · wild_boar · bear · monkey · bird · human · blank

Data modes:

• Simulation — CIFAR-10 remapped to wildlife classes (fast prototyping)
• Real — Folder-based wildlife image dataset (iWildCam or custom)

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Technical Highlights

• Spike raster plots — visualize neural firing patterns per layer and timestep
• Pareto frontier — accuracy vs. energy trade-off visualization across all modes
• ONNX export — SNN exported as single-step (T=1) for deployment compatibility
• Dynamic quantization — INT8 post-training quantization for edge deployment
• AMD GPU support — ONNX Runtime with VitisAI/DML/ROCm provider detection
• Reproducible — full seeding (torch, numpy, random, CUDA, PYTHONHASHSEED)

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Honest Limitations

• Spike count is a relative energy proxy, not absolute joule measurements. Actual hardware energy savings depend on deployment platform (neuromorphic chip vs. conventional GPU/CPU).
• CIFAR-10 simulation mode is for rapid prototyping. Real iWildCam data is needed for production evaluation.
• SNN→ONNX export uses single-step inference (T=1). Full temporal spiking dynamics are preserved in training but collapsed for deployment compatibility.

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License

MIT License — see LICENSE for details.

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Neuromorphic computing for conservation — because wildlife deserves efficient AI.