[FEATURE] Implement Multimodal Spam Detection Using NeuroEvolution of Augmenting Topologies (NEAT)

[BhakktiGautam]

Labels: feature, SSoC26, hard, multimodal

##Description
Spam attacks now span text, images, and voice . Attackers hide malicious content in images and audio to evade text-based filters . NEAT-dyMO achieves significant efficiency advantages over Transformer models while surpassing static baselines .

##Modalities
Image Spam: Obfuscated visual content with noise, fragmentation
Voice Spam: Robocalls, vishing with synthesized speech
Text Spam: Traditional email/SMS spam

##Impact
Critical: Attackers shift strategies across modalities
High: Single-modality detectors are failing
Urgent: Need multi-modal detection framework

##Suggested Fix
python

backend/multimodal_detector.py

class MultimodalSpamDetector:
def init(self):
# Evolving neural architecture
self.neat = NEAT(dynamic_weighting=True)

def detect_text(self, text):
    return self.neat.classify_text(text)

def detect_image(self, image):
    return self.neat.classify_image(image)

def detect_voice(self, audio):
    return self.neat.classify_voice(audio)

def ensemble_detect(self, modalities):
    # Multi-objective optimization
    scores = []
    if 'text' in modalities:
        scores.append(self.detect_text(modalities['text']))
    if 'image' in modalities:
        scores.append(self.detect_image(modalities['image']))
    if 'voice' in modalities:
        scores.append(self.detect_voice(modalities['voice']))
    return self.dynamic_weighting(scores)

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[github-actions]

The text proposes a feature to improve spam detection across multiple modalities, including text, images, and voice, using NeuroEvolution of Augmenting Topologies (NEAT). It highlights the limitations of single-modality detectors and static models in combating sophisticated spam strategies. The suggested solution involves implementing a dynamic, multi-modal detection framework leveraging neural architecture evolution to address obfuscation techniques used in modern spam attacks.